DCD>Magazine Issue 33 - Phase-Change Memory

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Contents July 2019

6 News US vs Huawei Apple’s Danish retreat 12 Phase-change memory On the cover: Behind IBM’s decadeslong plan to use in-memory computing to smash the von Neumann bottleneck, and usher in a new age of deep learning

Industry interview

12

16

16 B rian Cox, Stack Infrastructure “I think the cloud over time will win, but I have no problem selling to enterprise. If a customer comes in and says, ‘I want to deploy some into the cloud, and some on site,’ I’m not turning that business away.”

18 The risks of AI With great power comes great responsibility

39 55 19

19 At the Edge of the future A special supplement tackling the fundamental questions surrounding Edge computing - from the cost, to the connectivity, to how it is processed, and more.

Hear from industry experts, and from DCD’s readers, to learn where the Edge will live.

39 Smartening up How data centers use AI to keep cool, lower power, and optimize workloads 42 The cloud won’t kill you Enterprise data centers have life in them yet, Uptime reports

43

43 The San Francisco show preview What to watch, and where to go. Plus speaker Q&As for those who couldn’t make it 51 More than just stacks of Macs in racks Putting Apple hardware in a data center is more complicated than you might think 55 Servers need more memory Ram in as much RAM as you can

51 44

58 DCD crossword #1 And now for something a little different

Issue 33 ∞ July 2019 3

Phase change for AI hardware?

P

hase-change memory (PCM) is is more than just a new technology for cacheing bits and bytes. IBM hopes that by using it in analog chips which store data differently, it can blow past a long-standing barrier to cheap, fast AI processing: the von Neumann bottleneck.

Does that sound esoteric? Sebastian Moss has been finding out from the research leaders why we all need to pay attention (p12)

Never mind the robot uprising, AI faces legal and business risks Pros and cons of AI. By providing greater intelligence in packages which can be easily deployed, machine learning (ML) and artificial intelligence (AI) promise to make big changes to how we live - and data centers are making use of it (p39).

Google famously claimed to have shaved 40 percent off its cooling by handing real time control of cooling systems over to pattern-matching systems which could respond to new conditions. Now those savings are becoming available to data center operators across the board.

But before you implement AI, make sure you consider the risks. We're not talking about a robot rebellion here: like any new technology, AI has potential business drawbacks including lock-in and legal jeopardy (p18).

Ready for the Edge? A new wave of applications demand that storage and processing is done close to the application, for low latency. But how do we afford this and deliver it?

In this issue (p19), we've found time and space to kick the tires of the new concept. After years of centralizing IT resources in the cloud to get the economies of scale, do we really have to distribute them in more-costly chunks?

And how secure is it to process data in small micro facilities that could be carried off by a determined attacker?

For these issues and more, read our Edge supplement.

75%

From the Editor

Amount of enterprise data processed at the Edge by 2025, according to Gartner

4

Deputy Editor Sebastian Moss @SebMoss Reporter Will Calvert @WilliamLCalvert SEA Correspondent Paul Mah @PaulMah Brazil Correspondent Tatiane Aquim @DCDFocuspt Head of Design Dot McHugh Designer Mandy Ling Head of Sales Martin Docherty

Conference Producer, EMEA Zach Lipman Chief Marketing Officer

At our San Francisco event in July

Dan Loosemore

(p43), we are expecting to learn more about the ways in which data center building has to change, to keep up with demand from AI, from Edge, and all the other new requirements. Read our preview to make your plans.

Head Office DatacenterDynamics 102–108 Clifton Street London EC2A 4HW +44 (0) 207 377 1907

Apple hardware is famously a law unto itself. So why on earth would anyone use it in a data center, where low price and commoditization are generally the only requirements?

The answer is that businesses delivering on Macs need access to Macs on demand. The answer is specialized racks, and a unique data center provider: we found out more from MacStadium (p51).

PEFC Certified This product is from sustainably managed forests and controlled sources PEFC/16-33-254

For something different, go to our cryptic crossword on p58. It's a new departure - tell us how we did.

Oh, and speaking of departures, Max Smolaks is back, with a look at memory.

Peter Judge DCD Global Editor

bit.ly/DCDMagazine

Follow the story and find out more about DCD products that can further expand your knowledge.

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Intelligence

Global Editor Peter Judge @Judgecorp

Conference Director, NAM Kisandka Moses

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Whitespace

News

NEWS IN BRIEF

Whitespace: The biggest data center news stories of the last two months

Microsoft opens first Middle Eastern Azure regions in Abu Dhabi and Dubai This year, Microsoft also obtained permission from the Qatari cabinet to build a cloud region in the Gulf country, but it has yet to make an official announcement.

Wärtsilä announces modular gas powered generator “Modern gas engines can start in less than a minute, which brings them within the world of emergency power supply,” Wärtsilä senior application manager Juha Kerttula told DCD.

Google shifts server motherboard production out of China The decision was made to avoid the 25 percent tariff levied by the US against some Chinese goods, as well as to reduce the risk of being at the mercy of Beijing, Bloomberg reports.

US levels trade ban against Huawei American companies can’t do business with Chinese telco The Commerce Department has banned US companies from selling goods or services to Huawei or its affiliates, without government approval.

A short 90-day reprieve until August 19 exists for some support sales and services, but is limited in scope.

US companies including Intel, Qualcomm, Xilinx, Broadcom, Qorvo, Micron Technology, Western Digital, and Lumentum Holdings are all thought to have ceased sales to Huawei.

Google has also ended its Android licensing agreement for the provision of Google Play Services and access to the Google Play Store on new Huawei Android devices, but the company can continue to use the Android Open Source Project (AOSP).

Non-American companies that use US manufacturing or supplies, like Infineon Technologies, have halted the sales of some products. Semiconductor designer Arm has also suspended business with Huawei, a major development which will impact the design of future Huawei chips, including its Kirin smartphone

6

processors, and its Kunpeng 920 server CPU. “The current practice of US politicians underestimates our strength. Huawei’s 5G will absolutely not be affected,” company founder and CEO Ren Zhengfei said. “In terms of 5G technologies, others won’t be able to catch up with Huawei in two or three years. We have sacrificed ourselves and our families for our ideal, to stand on top of the world.”

While the US has long claimed Huawei poses a security threat - something the company denies - the Commerce Department gave another reason for the ban.

It cited a Department of Justice indictment against the company and several employees for allegedly conspiring to evade US sanctions against Iran.

In July, President Trump signalled a willingness to remove sanctions, but did not provide concrete details.

For a detailed explanation of the history of US-Huawei tensions and allegations, go here: bit.ly/DontCallItaColdWar

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Eaton launches small UPSaaR pilot in Dublin, Ireland The company hopes to prove that data centers, and other large electricity users, can benefit from using their uninterruptible power supplies to help the grid. The company’s pilot is small, using only a 200kW UPS, and comes two years after the service was first announced.

Regal Orion is building a $290 million Malaysian data center The Malaysia-based Japanese-owned company acquired the partiallycompleted data center in Labu, Negeri Sembilan last year for $16m (RM69m) and are now working to complete the building.

The first phase is expected this year.

Related News: Huawei plans to sell its submarine cable business The company will sell its majority-stake in submarine cable business joint venture Huawei Marine Systems to Chinese optical telecommunication network products company Hengtong Optic-Electric Co. In March this year, the WSJ detailed how the US government was trying to paint Huawei Marine as a security threat.

IBM to cut 1,700 jobs

Apple cancels $921m second Danish data center

READ MORE A different kind of Apple data center, p51

Will focus on its first facility Apple has canceled plans to build a $921m data center in Aabenraa, Denmark, and instead will expand its upcoming Viborg data center.

The 285-hectare site, originally purchased by the company in 2017, will be put up for sale.

“As we near completion of our new Viborg data center in central Jutland, Denmark, we’ve decided to focus on growing that site instead of building an additional data center in Aabenraa,” Apple said in a statement.

In a post on its official website, the Aabenraa Municipality called the decision “completely unexpected.” Director Stig Isaksen wrote (translated): “It is my view, from my conversation with Apple today, that this is an overall strategic business decision made in the United States, and that the decision has been taken entirely independently of the circumstances of the Aabenraa Municipality.”

Mayor Thomas Andresen added: “This is undoubtedly an undesirable bump on the way for Aabenraa Municipality’s long-term efforts to create jobs and increased settlement out of the establishment of data centers in Aabenraa Municipality. Fortunately, Apple is not the only player in that market, and as our data center

strategy clearly describes, we have a targeted strategic effort aimed at, among other things, two data centers in operation and two in planning in 2022.”

Google also owns 131 acres in the municipality, but has no immediate plans to build a data center there.

Apple will instead focus on its Viborg facility, which is behind schedule. In April it was revealed that the company had fired the project’s main contractor, and work on the facility had ceased. It is unclear what is the current state of the data center, originally planned to open in late 2017. The company has suffered a rocky development path for its European data centers, spending years trying to build a facility in Galway, Ireland, before canceling the project after years of delays.

In addition to its own facilities, Apple uses AWS, Google Cloud, colocation facilities, and small cloud companies. Earlier this year, CNBC reported that the company had signed an agreement to spend at least $1.5 billion on AWS over the course of five years. bit.ly/ADenmarkADayKeepsTheAppleAway

IBM will lay off around 1,700 employees, CNBC reports.

The company said in a statement to Bloomberg that the cuts would affect a “small percentage of employees” who are not performing “at a competitive level.”

At the end of last year, IBM’s workforce totaled around 350,000 people, so these layoffs would cut less than one percent of the company’s employees.

In a statement to CNBC, IBM said: “We are continuing to re-position our team to align with our focus on the high-value segments of the IT market, and we also continue to hire aggressively in critical new areas that deliver value for our clients and IBM.” The company’s career page is currently advertising 7,310 jobs globally.

In 2016, IBM started culling jobs to redeploy staff in other areas that the company wanted to focus on. This process involves making job roles redundant and allowing the dismissed workers to reapply for open jobs elsewhere in the organization.

Unofficial reports at the time estimated that up to a third of the company’s workforce was ‘rebalanced’ in this way. bit.ly/ButHasMoneyForRedHat

Microsoft, AWS, Equinix, QTS & more criticize Dominion’s fossil fuel plans in Virginia Some of the world’s largest data center and cloud companies have called for Dominion Energy to scrap its plans to invest in natural gas infrastructure and instead focus on renewable energy and energy storage capacity to help power the data centers of the huge hub in Northern Virginia.

Adobe, Akamai Technologies, Apple, AWS, Equinix, Iron Mountain, LinkedIn, Microsoft, Salesforce and QTS delivered a letter criticizing the company during Dominion Energy’s integrated resource plan hearing at Virginia’s State Corporation Commission (SCC), where it unveiled its proposed plans for the next decade.

More than 70 percent of the world’s Internet traffic is thought to flow through data centers in Northern Virginia. bit.ly/BecauseDominionIsAFriendlyName

Whitespace

European Processor Initiative delivers first architectural designs Europe’s RISC-V chip for exascale gets closer The European Union’s ambitious project to develop a low-power microprocessor for exascale supercomputers, data centers and the automotive market has passed its first major mileston.

After launching in December last year, the EPI has delivered its first architectural designs to the European Commission.

The EPI plans to build a European microprocessor using RISC-V architecture, embedded FPGA and Arm components, ready for a pre-exascale system in 2021, and an upgraded version for the exascale supercomputer in 2022/23. bit.ly/RISCyBusiness

NEWS IN BRIEF

Nvidia to fully support Arm, says platform has “reached a tipping point” for HPC adoption Once the optimization for its full stack of AI and HPC software is complete by the end of the year, Nvidia will support all major CPU architectures, including x86, Power and Arm.

Lenovo to lay off 500 employees globally, data center division hit hard Sources speaking to WRAL said that the job cuts will hit 20 percent of Lenovo’s 200-person Data Center Group, its fastest growing business division.

Intel to acquire programmable chipmaker Barefoot Networks Intel plans to acquire data center networking company Barefoot Network, which makes programmable processors as well as Ethernet switches and software.

Data is the most valuable asset of our times.

Photo: Per Pixel Petersson/imagebank.sweden.se

DATA CENTERS

BY SWEDEN

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1.5 exaflops Frontier supercomputer coming in 2021 from Cray and AMD Related News

HPE to acquire Cray for $1.3 billion Supercomputer consolidation Hewlett Packard Enterprise has entered into a definitive agreement to acquire supercomputing company Cray for $1.3 billion in cash.

The transaction is expected to close by the first quarter of HPE’s fiscal year 2020, subject to regulatory approvals and other customary closing conditions, and comes after HPE acquired rival supercomputing company SGI in 2016 for just $275m.

For its first quarter of 2019, ended March 31, Cray’s revenue was $72 million, down from $80m the year before. It made a net loss of $29m, up from $25m. Earlier this year, it warned of “a substantial net loss for both 2018 and 2019,” following an industrywide market contraction.

“Answers to some of society’s most pressing challenges are buried in massive amounts of data,” Antonio Neri, president and CEO of HPE, said.

“Only by processing and analyzing this data

will we be able to unlock the answers to critical challenges across medicine, climate change, space and more... By combining our world-class teams and technology, we will have the opportunity to drive the next generation of high performance computing and play an important part in advancing the way people live and work.”

HPE said that it expects $4 billion of exascale opportunities to be awarded over the next five years.

The company said that the acquisition would allow it to offer future HPC-as-a-Service and AI/ ML analytics through HPE GreenLake, gain a larger footprint in federal business and academia and to “deliver significant cost synergies through efficiencies and by leveraging proprietary Cray technology, like the Slingshot interconnect.”

bit.ly/EpycSupercomputer bit.ly/ThenThereWasOne

IBM builds world’s most powerful commercial supercomputer for Total IBM has built the world’s most powerful commercial supercomputer for Total, one of the seven “supermajor” global oil companies.

The Pangea III requires 1.5 megawatts, compared to 4.5MW for its predecessor system. Built with IBM Power9 CPUs and Nvidia Tesla V100 GPUs, the Pangea III has a claimed performance of 31.7 petaflops.

IBM said that “as the global demand for energy continues to grow, finding new oil reserves that can be profitably extracted is a top priority.” That statement comes after climate scientists have for years been begging governments to put a high priority on not exploiting any new oil reserves and, in fact, leaving them exactly where they are. bit.ly/NotNowIBM

The US Department of Energy’s Oak Ridge National Laboratory will be home to a supercomputer capable of more than 1.5 exaflops of performance in 2021.

Frontier will be based on Cray’s new Shasta architecture and Slingshot interconnect, and feature upcoming AMD Epyc CPUs and Radeon Instinct GPUs. The supercomputer will consist of more than 100 Cray Shasta cabinets, with densities of up to 300kW per cabinet, and a 4:1 GPU to CPU ratio. In total, it will use more than 90 miles (145km) of cabling in a 7,300 square feet (680 sq m) data center.

The full contract award for what will be the world’s most powerful computer is valued at more than $600 million. 2021 is also when Argonne National Laboratory will deploy Aurora, a one exaflops system from Cray and Intel.

Whitespace

Google suffers outage, impacts Cloud, YouTube, Gmail and more

BGP leak leads to short WhatsApp outage Facebook’s messaging platform WhatsApp experienced an outage.

The June 7 issue was the result of a major BGP route leak by colocation provider Safe Host. The Swiss company leaked thousands of prefixes which had a cascading effect on the availability of those services when the routes were accepted and propagated by service providers, such as China Telecom, and then further accepted by other ISPs such as Cogent.

When traffic for WhatsApp reached Cogent, it sent the traffic to China Telecom, which led to significant packet loss, possibly due to aggressive filtering policies of the Great Firewall.

The route leak appears to have been a benign accident, rather than an intentional BGP hijack.

BGP route leaks are not uncommon on the Internet, with a similar incident in November 2018 causing widespread outages across Google’s portfolio of products and services. That time, MainOne accidentally sent traffic to China Telecom.

“Okay Google, are you okay?”

Google Cloud experienced widespread issues on Sunday, June 2, impacting the search giant’s own services, as well as that of its cloud customers.

The intermittent four-hour outage was blamed on “high levels of network congestion.”

Google services like YouTube, Nest and Gmail, as well as Cloud customers like Snapchat, Shopify, Vimeo and Discord were impacted.

Google’s VP of 24x7, Benjamin Treynor Sloss, said: “In essence, the root cause of Sunday’s disruption was a configuration change that was intended for a small number of servers in a single region.

“The configuration was incorrectly applied to a larger number of servers across several neighboring regions, and it caused those

bit.ly/StrongAndStable

Peter’s outage factoid Outages are said to cost $9,000 per minute (Ponemon) but this is a simplification, and short outages are less damaging.

A five minute break costs $2,600 according to ITIC

bit.ly/WhatsOut

Target blames global checkout outage on NCR data center issue Checkout machines at retail giant Target were unusable for nearly two hours on June 16, due to an issue at a data center.

The unidentified problem with a facility used by vendor NCR caused the downtime, Target said, but added that the issue was unrelated to a point-of-sale machine outage the day before.

That time, the failure was caused by an error made during regular system maintenance.

regions to stop using more than half of their available network capacity.

“The network traffic to/from those regions then tried to fit into the remaining network capacity, but it did not.”

ThousandEyes’ Angelique Medina told DCD: “One of the most important takeaways from cloud outages is that it’s vitally important to ensure your cloud architecture has sufficient resiliency measures, whether on a multi-region basis or even multi-cloud basis.”

Google said: “Please rest assured that system reliability is a top priority at Google, and we are making continuous improvements to make our systems better.”

“Like many other companies, Target uses NCR as a vendor to help accept payments, and on Sunday afternoon NCR experienced an issue at one of their data centers,” Target spokesperson Jenna Reck said in a statement.

DCD contacted NCR for further details about the specifics of the issue, but it did not provide any comment. bit.ly/ShopTillitDrops

10 DCD Magazine • datacenterdynamics.com

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Cover Feature | Beyond von Neumann

PHASECHANGE MEMORY IBM plans to smash the von Neumann bottleneck, and usher in a new age of deep learning

12 DCD Magazine • datacenterdynamics.com

Sebastian Moss Deputy Editor

W

e live in a world built on the back of enormous technological advances in processor technology, with rapid increases in computing power drastically transforming our way of life.

This was all made possible thanks to three key factors: The von Neumann architecture that the vast majority of processors are based on; Moore’s Law, which predicted the trend of increased transistor count, leading to more functionality on the chip at a lower cost; and Dennard scaling, laws on how to make those transistors smaller while their power density stays constant, allowing the transistors to be both faster and lower in power.

But the rapid growth made attainable by technology solutions and scaling is nearing an end. The death of Moore’s Law is often pronounced, as chip companies struggle to shrink transistors beyond the fundamental limits of how small they can go. Device scaling has slowed due to power and voltage considerations, as it becomes harder and harder to guarantee perfect functionality across billions of devices.

Then there’s the von Neumann bottleneck. The von Neumann architecture separates memory from the processor, so data must be sent back and forth between the two, as well as to long-term storage and peripheral devices. But as processor speeds increase, the time and energy spent transferring data has become problematic,

leaving processors idle and capping their actual performance. This problem has become particularly acute in large deep learning neural networks, limiting the potential performance of artificial intelligence applications.

Yearning to move on from von Neumann’s grand designs of the 1940s, an ambitious effort is underway at IBM to build a processor designed for the deep learning age.

Using phase-change memory devices, the company hopes to develop analog hardware which performs a thousand times more efficiently than a conventional system, with in-memory computing on non-volatile memory finally solving the bottleneck challenge.

But this new concept brings its own set of complex technological hurdles yet to be overcome.

In a series of interviews over the past six months, IBM gave DCD a deep dive into its multi-year project underway at its labs in Zurich, Tokyo, Almaden, Albany and Yorktown.

Handling analog information "There are many AI acceleration technologies that we're looking at in various states of maturity,” Bill Starke, IBM Distinguished Engineer, Power microprocessor development, told DCD. “This is the most exciting one I've seen in a long time.”

Phase-change memory (PCM) “was originally meant to be a memory element which just stores zeros and ones,” Starke said.

A Simple Neural Network Input Layer

Hidden Layers

Output

Weights

"For the price of a memory read, you're essentially doing a very complex matrix operation, which is the fundamental kernel in the middle of AI” “What was recognized, discovered, invented here was the fact that there's an analog physical thing underneath it,” which could be used for processing deep learning neural networks as well as for memory.

“For the price of a memory read, you're essentially doing a very complex matrix operation, which is the fundamental kernel in the middle of AI,” Starke said. “And that's a beautiful thing, I feel like nature is giving us a gift here.”

Exploiting the unique behavior of chalcogenide glass, phase-change memory can - as the name suggests - change its state.

Chalcogenide glass has two distinct physical phases: a high conductance crystalline phase and a low conductance amorphous phase.

Both phases coexist in the memory element.

The conductance of the PCM element can be incrementally modulated by small electrical pulses that will change the amorphous region in the element.

The overall resistance is then determined by the size of the amorphous regions, with the atomic arrangement used to code information. “Therefore, instead of recording a 0 or 1 like in the digital world, it records the states as a continuum of values between the two - the analog world," IBM notes.

The company has been researching PCM for memory for more than a decade, but "started building experimental chips for AI applications in 2007-2008," Evangelos Eleftheriou, Zurich-based IBM Fellow, Neuromorphic & In-memory Computing, told DCD. "And we keep producing experimental chips - one of those is the Fusion chip, and we have more in the pipeline."

Training and inference To comprehend how different chip architectures can impact deep learning workloads, we must first understand some u

Issue 33 • July 2019 13

u of the basics of deep learning, training and inference.

Think of a deep learning neural network as a series of layers, starting from the data input and ending with the result. These layers are made up of groups of nodes that are connected with each other, loosely inspired by the concept of neurons in the brain. Each connection has an assigned strength or weight that defines how a node impacts the next layer of nodes.

During the training process the weights are determined by showing a large number of data, for instance images of cats and dogs, over and over again until the network remembers what it has seen.

The weights in the different layers, together with the network architecture, comprise the trained model that can then be used for classification purposes. It will be able to distinguish cats from dogs, giving a large weight to relevant features like whiskers, and will not be disturbed by irrelevant low-weight features like, for instance, clouds in the picture.

This training phase is a hugely complex and computationally intense process, in

"We see a very large advantage in overall compute efficiency...

These techniques will allow one of the most compute intensive parts of the computation to be done in linear time" which the weights are constantly updated until the network has reached a desired classification accuracy - something that would be impractical to run every single time somebody wanted to identify a cat. That’s where inference comes in, which takes a trained model and solidifies it, running it in the field and no longer changing the weights.

More work for less power The long term aim, according to Jeff Burns, IBM Research’s director of AI Compute, is for PCM to be able to run both inference and training workloads.

"We see a very large advantage in overall compute efficiency," said Burns, who is also the director of the company’s upcoming AI Hardware Center in New York. "So that can

be realized as: if you have a certain workload, doing that workload at much, much, much lower power consumption. Or, if you want to stay in a power envelope, doing a much larger amount of computation in the same power. These techniques will allow one of the most compute intensive parts of the computation to be done in linear time.”

By carefully tuning the PCM devices' conductance, analog stable states can be achieved, with neural network weights memorized in the physical phase configuration of these devices. By applying a voltage on a single PCM, a current equal to the product of voltage and conductance flows. IBM researchers Stefano Ambrogio and Wanki Kim explain: "Applying voltages on all the rows of the array causes the parallel summation of all the single products. In other words, Ohm’s Law and Kirchhoff’s Law enable fully parallel propagation through fully connected networks, strongly accelerating existent approaches based on CPUs and GPUs." But this tantalizing promise of a superior technology that breaks free from the von Neumann bottleneck, that outlives Moore’s Law, and ushers in new deep learning advances, comes with its own set of issues.

"The problems are different in both inference and training," Wilfried Haensch, Distinguished IBM Research staff member, Analog AI technologies, told DCD. Let's start with the comparatively easier inference space, and assume you still run training workloads on a GPU. "So, the trick here is, how do you get the weights from the GPU environment onto the analog array, so that you still have sufficient accuracy in the classification?” Haensch said.

"This sounds very easy if you look at it in a PowerPoint slide. But it's not. Because if you copy floating point numbers from one digital device to another, you maintain the accuracy - the only thing that you do is copy a string of zeros and ones.”

Analog is less accurate When copying a number from a digital environment into an analog environment, things become a little more complicated, Haensch said: “Now what you have to do is take the strings of zeros and ones, and imprint it into a physical quantity, like a resistance. But because resistance is just a physical quantity, you will never be able to copy the floating point number exactly.

Physics is precise but not accurate, so you get a precise resistance, but it might not be the one that you want - perhaps it's a little bit off."

This inference accuracy issue is

14 DCD Magazine • datacenterdynamics.com

something IBM's Almaden lab hopes to overcome, running tests on long short-term memory (LSTM) networks, a complex deep learning approach fit for tasks with sequential correlation like speech or text recognition, where it can understand a whole sentence, rather than just a word.

In a paper presented at the VLSI Symposia this June, Inference of Long-Short Term Memory networks at software-equivalent accuracy using 2.5M analog Phase Change Memory devices, Almaden “deals with how to copy the weights into the neural network and maintain inference accuracy,” Haensch said.

The paper details how to use an algorithm that allowed researchers “to copy the weights accurately enough, so that we can maintain the classification accuracy, as expected from the floating point training,” Haensch said.

“So this is a very, very important point.

Our philosophy is that we will first focus on inference applications, because they're a little bit easier to handle from a material perspective. But if we want to be successful with this, we have to find a way to bring the trained model into the analog array. And this is a significant step to show how this can be done.”

For inference PCM devices, IBM have “convinced themselves that this approach is feasible and that there is no fundamental roadblock in the way,” Haensch said. “For commercial use, inference is probably about five or six years away.”

Can analog devices do training?

After that comes training, with Haensch admitting that “the training part is a little bit out. You really have to re-engineer these non-volatile memory elements so that they have certain switching behavior.”

Over in the Zurich labs, researchers got to work on trying to overcome the inherent challenges with PCM devices for deep learning training.

“In deep learning training, there are basically three phases,” Zurich’s Eleftheriou told DCD. “There is a forward pass, that is similar to inferencing, in which you don't stress precision,” where you calculate the values of the output layers from the input data with given fixed weights, moving forward from the first layer to the last layer.

“Then there is a backward pass with errors, again you don't need high precision,” where computation is made from the last layer, backward to the first layer, again with fixed weights, he said.

The third part is where you “need to update the weights, thereby changing the connection strength between the input and

Cover Feature | Beyond von Neumann

See the light The race to escape von Neumann's bottleneck for AI workloads has many contenders, each trying radically different approaches.

One potential solution is that of optical neural networks, first proposed by Yichen Shen et al. at MIT in 2017. The concept of programmable nanophotonic processors could allow for light to be used for matrix multiplication, the most power hungry and time consuming part in AI algorithms.

In May 2019, Intel said it had built upon Shen's research, in collaboration with UC Berkeley, and come up with realworld workarounds for the challenges of manufacturing such complex systems, but added that much more work was still required.

output of each layer,” Eleftheriou said. It is this part that remains difficult, so the solution at Zurich is to run the first two phases of training - forward and backward passes - on the PCM. However, the weight updates are accumulated on a standard von Neumann processor before the updates are transferred rather sporadically to the PCM devices. “This is, in a nutshell, the whole idea."

Haensch said: “That's a very important stepping stone, because it allows us to create the ability to train without pushing the material properties too hard.”

Cindy Goldberg, program director of AI Hardware Research at IBM, concurred: “It’s about not just looking at the exact workloads of today, but how these AI workloads are evolving out of these very narrow applications into much more broad and evolving and dynamic AI workloads.

“This is what informs the development approach of these accelerators, it is not just about having the best widget of today that is out of date in six months, but about really anticipating how these workloads are evolving and changing for the long term.”

Going commercial Going beyond that stepping stone, as well as pushing towards commercialization, could have a profound impact on the future of deep learning, Haensch believes.

“If you look at the development of neural networks today, it is really driven by the properties of GPUs,” he said. “And GPUs require that you have narrow and deep networks for better learning - this is not necessarily the best solution. The analog arrays allow you to go back to shallow networks with wider layers, and this will open up the possibility to to re-architect deep learning for learning optimization, because you are not bound anymore by the memory limitations of the GPUs.”

DCD>Debates Are we ready for a high-density AI future?

On Demand

The advent of GPUs and high-density computing gives enterprises a fantastic opportunity to run AI applications that can unlock significant business benefits - but at a cost. Learn how to deal with high-density systems with difficult power and cooling challenges in this webinar, featuring McMaster University's Dr Suvojit Ghosh and ScaleMatrix's Chris Orlando. bit.ly/AreYouAIReady

Issue 33 • July 2019 15

CEO Interview | Brian Cox, Stack Infrastructure

Wholesale Invention What happens when you set up a colocation provider aimed at wholesale customers - and they still ask for custom deals? Stack Infrastructure’s CEO Brian Cox talks to Peter Judge

Peter Judge Global Editor

the assets of colo providers Infomart and T5 Data Centers, and reorganized them.

The retail colo spaces remained with T5, and the Infomart assets were merged with T5’s wholesale colo ventures, to form a new provider focusing on wholesale: Stack Infrastructure.

C

olocation is changing.

Enterprises in large numbers still want to relocate their own servers to third party “retail colo” facilities, but at the same time, webscale cloud service providers are snapping up colocation space wholesale, in massive slices, and then using that to serve customers including those enterprises.

Most colocation providers argue that these two models are compatible, though perhaps not in the same buildings, but it does seem there is a divergence here, between providing whole rooms swiftly, and managing individual customers at the rack and row level.

At the beginning of 2019, the appearance of Stack Infrastructure was a very visible sign of this split. Investors IPI Partners took

16 DCD Magazine • datacenterdynamics.com

Stack started out with 100MW of data centers, but more importantly, a lot of land in key locations including Northern Virginia and Texas, with projects already underway, and space to build more giant facilities.

It also acquired a CEO with eight years of experience at wholesale colo provider Cologix: Brian Cox. Cox agrees that Stack’s wholesale focus mirrors the industry: “I think it's challenging right now to serve both retail and wholesale,” he said, when we met him at DCD>New York in April. But he insists most providers (including Stack) are still not 100 percent exclusively wholesale. “I do see some people who have gone wholesale. But then they have some extra space, so they productize it as a retail environment.”

He argues that a wholesale provider who offers some retail is a different animal from a retail provider who aspires to serve the wholesale market, because the two markets require a different mindset: success in wholesale is difficult to achieve, unless wholesale colocation is your primary focus.

Stack has one facility that doesn’t fit this mold: the Dulles Infomart site, which DCD visited in 2018. Marketed as wholesale colo, it is a heritage building created by AOL at

Even having just sold a completely standard hall or building, he says it’s important to respond to these requests: “If I were to stamp out a configuration with each one being a certain building block, I might have lost that opportunity.”

It’s a balancing act though, because even as they apply requests and requirements, the customers still want their space quickly.

That’s not something that can be changed, he said, because fast timescales are more than a whim of the customers.

They are built into the new applications which are causing providers to build at the current unprecedented rate.

All the big demand drivers that are coming up, such as AI, are essentially both unpredictable and unavoidable, said Cox: “I can't tell you what the demand curve is going to be for machine learning or AI or autonomous vehicles. All I can tell you it's going to be up and to the right.”

the birth of the commercial Internet, and it’s available in chunks of around 2MW. Everywhere else, Stack is all about building new capacity, and normally offering it 8MW at a time, while this is an existing building - which never have been picked up by Infomart (and passed on to Stack) were it not for its location, its connections, and its knock-down price.

Aside from Dulles, Stack is in major colocation markets, including Texas, Georgia, Chicago, Portland and LA: “We're in the markets that our customers want us to be in,“ he says.

The company’s approach, like other wholesale providers, leans towards standardization. That’s the only way to deliver space in the timescales customers want, and at a profit. But Cox says Stack swims against the stream somewhat, by offering somewhat more customization than most.

The enabler is mutual flexibility. Customers who need space now will have no option but to buy something that is currently available: this means buying into existing buildings, whose layout and architecture are fixed. In this case, they obviously have little say over what provision they get. But Cox says those same customers will then commit to projects which are still to be built, and here they get a lot of say on the design.

“Customers say ‘We'll take what you have now - but when are you putting up the next building on the campus?’ And at that point they start to make requests,” Cox told us.

If people are buying capacity, and that’s all they have to go on, then wholesale colo providers (and in the retail space, operators like Equinix) take a Fordist approach and deliver highly standardized resources, and deliver them very quickly.

“Most people say the only way to meet this market is to define exactly what it's going to be - and be completely standard,”

“I think it's challenging right now to serve both retail and wholesale. Some people have gone wholesale - but have extra space, and productize it as a retail environment” said Cox. “Rate and timeline to delivery are KPIs that all the customers are expecting.

So how can I be more flexible on this, in the same footprint? That's my challenge.”

The accepted wisdom in wholesale colocation is that you can get a building in eight months, if you accept exactly what the builder offers. But Cox says there are customers who want to get involved in the

process, and understand that customization comes with a cost and time penalty. He is prepared to innovate at the room level, inside the data hall but not at the rack or row. “What I'm saying is it probably stops at the data hall, where your flexibility really begins in terms of product, it's inside the critical infrastructure space.”

The only way to sell this is straight talking: “You have to be transparent to your customer. That should be the goal of the entire industry.”

That should be a simple decision, he said: “Why wouldn't you be transparent with your customers? They know more about the cloud. And they're benchmarking me against four others who can deliver similar capabilities.”

As it turns out, Stack does have some retail in its T5 facilities, used by enterprises that still have in-house resources, but Cox has a plan to manage this dual solution. That plan is “not to compete with the cloud,” because the cloud is the future.

“I think the cloud over time will win,” he said, “but I have no problem selling to enterprise. If a customer comes in and says, ‘Look, I want to deploy some into the cloud, and some on site,’ I'm not turning that business away, because I recognize the business problem they're trying to solve.”

There will always be a subset of Stack’s customers that needs its own space for its part of the hybrid cloud, and however fast the cloud is growing, this space is still growing - albeit at a slower rate.

If it takes new thinking, at least Stack may have backers that could allow that. It’s not a real estate investment trust (REIT) like Digital Realty, and it’s not owned by a multinational telco like RagingWire.

It’s been set up with backing from Iconiq Capital, an enigmatic investor with money from tech billionaires, who made money from the FAANGs (firms including Facebook, Apple, Amazon, Netflix and Google). They are the kind of people who get described as “Silicon Valley royalty,” though Cox rejects that term.

Stack is funded by IPI, a joint venture between Iconiq and real estate investors Iron Point, and Cox says “IPI has a thesis,” pointing out that Iconiq should bring in people with understanding of the hyperscale world: “It's a magic combination of money and star quality... and investors who know the space. When I was voting with my own career, I saw that as a winning hand.”

Some operators have to explain the arcane world of digital infrastructure to backers who only understand money; Cox says he is far more likely to get calls from the investors telling him the next opportunity he should be attacking.

That should keep him on his toes.

Issue 33 • July 2019 17

Human Opinion | AI Risk

Data center AI creates new risks AI is a powerful tool, but with power comes risk, warns Rhonda Ascierto

A

rtificial intelligence (AI) is being used in data centers to drive up efficiencies and drive down risks and costs.

But it also creates new types of risks.

Some of these risks are not clear-cut.

Take, for example, new AI-driven cloud services, such as data center management as a service (DMaaS), that pool anonymized data from hundreds or thousands of other customers’ data centers. They apply AI to this vast store of information and then deliver individualized insight to customers via a wide area network, usually the Internet.

But that raises a big question: Who owns the data, the supplier or the customer? The answer is usually both: customers can keep their own data but the supplier typically also retains a copy. This means that, even if the paid service stops, the data remains an anonymous part of the supplier’s data lake.

Does this lack of clarity over data ownership constitute a risk to data centers?

The answer is vigorously debated. Some say that if hackers accessed data, it would be of little use as the data is anonymized and, for example, does not include specific location details. Others say hackers could apply techniques, including their own AI analysis, to piece together sensitive information to build up a fairly complete picture.

This is just one example of the risks that should at least be considered when deploying AI. Uptime sees four areas of risk with AI offerings:

Commercial risk: AI models and data are often stored in the public cloud and outside of immediate control (if using a supplier model). Even if they are on-site the models and data may not be understood.

Commercial machine learning products and services raise the risk of lock-in because processes and systems may be built on top of models using data that cannot be replicated.

Pricing may also increase as adoption grows. At present, prices are low to attract new data (to build up the effectiveness of AI models) or to attract equipment services or sales.

A high reliance on AI could change skills requirements or “de-skill” staff positions, which could potentially be an issue.

Legal and service level agreement risk: Again, AI models and data are stored outside of immediate control (if using a supplier model) or may be on-site but not understood. This may be unacceptable for some, such as service providers or organizations operating within strict regulatory environments.

In theory, it could also shift liability back to an AI service supplier - a particular concern for any automated actions provided by the service.

Technical risk: While we usually understand what types of data are being used for human actions and recommendations, it is not always possible to understand why and exactly how a machine reached a decision.

It may not be possible to easily change or override decisions.

As machines guide more decisions, core skills may become outsourced, leaving organizations vulnerable.

AI service prices are low now, to attract new data, but there is a risk of lock-in and incompatibility

Interoperability risk and other “unknown unknowns:” The risk from the development of “2001” HAL scenarios (i.e., singularity) are over-played but there is an unknown, longterm risk.

One example is that AI is likely to be embedded in most cases (i.e., inside an individual equipment and management system). This could lead to situations where two or three or five systems all have some ability to take action according to their own models, leading to a potential runaway situation - or conflict with each other.

For example, a building management system may turn up the cooling, while an IT system moves workload to another location, which turns up cooling elsewhere.

Data center operators are already applying AI in their facilities, and cloud providers intend to profit from AI services. These moves should be informed by an awareness of the possible downsides.

For more, read Very smart data centers: How artificial intelligence will power operational decisions, available to members of the Uptime Institute Network bit.ly/UptimeAIRisk

Rhonda Ascierto Uptime Institute

18 DCD Magazine • datacenterdynamics.com

Rhonda Ascierto is Vice president of Research at the Uptime Institute.

She has spent nearly two decades at the crossroads of IT and business as an analyst, speaker, adviser and editor covering the technology and competitive forces that shape the global IT industry.

> Edge | Supplement

Sponsored by

INSIDE

™

The cost of the Edge > Can the new layer of Edge infrastructure be cheaper than centralized cloud services?

The Edge of space

> The solution to connecting IoT devices might be much further away than you think

Processing the Edge

> Chip designers are battling to build the processors that will run the Edge

Edge Supplement

Sponsored by

Contents 22 The cost of the Edge: Small local resources lose the economies of scale 24 The Edge of Space: Sometimes a satellite is as close as you can get 26 Advertorial Why Edge capacity is needed, and how it will be deployed 28 The Edge of Things: IoT demands different resources 30 Reality check: The DCD community tell us their Edge attitudes 32 Processing the Edge: Is the hardware ready?

34 Increasing the attack surface: Are Edge resources more vulnerable?

36 Searching for Edge on 5G The biggest Edge application could be 5G itself

30

22 24

32

26

36

Making the Edge less nebulous

E

dge computing has received a huge amount of support and coverage. This supplement aims to start to give Edge the critical questioning it needs.

We all know the pitch: the Internet of Things (IoT), autonomous vehicles, and virtual reality all generate a lot of data and need a quick response. This adds up to a need for low-latency IT close to users and devices. So far, so plausible, but here are the most obvious gaps in that reasoning.

Is Edge cost-effective?

For more than twenty years, IT has been increasingly centralized, with economies of scale making cloud resources virtually free.

We've become so used to free IT, that Edge proponents can simply forget to factor in the costs.

It seems to be taken for granted that when Edge applications want resources, they will appear.

But small micro data centers lose the benefits of scale, and can't be massively virtualized (see p22).

For Edge to become real, the benefits of those Edge applications need to be set off against the actual costs of real distributed resources.

The economics of Edge need to be discussed.

Is Edge secure?

Another thing that is taken for granted is security. Edge applications will inevitably handle customer data, on servers outside the walls of traditional data centers.

Micro data centers, or smaller fragments of IT resource, are vulnerable to new dangers, such as being simply carried off. They will

need new security measures - and mature Edge deployments must test and deploy them (p34).

Is Edge really local?

Another really basic assumption of Edge is that its resources have to be local. But most Edge applications will rely on cellular networks, which still do not have global coverage.

Many Internet of Things (IoT) applications need near-universal coverage (p28). But this has to be delivered over today's networks or emerging 5G services (p36).

Right now, it's obvious that far from being served locally, some Edge applications will actually be delivered from the most remote resources we have: satellites in Low Earth Orbit (p24).

Is Edge hardware there?

Perhaps the biggest assumption about Edge is the belief that the hardware will be similar to what is in use today.

That may be true for the first, deployments, what we might call Edge 1.0. But tech hardware never stands still, and the pressures on economics and security will drive new models into use more quickly in a nascent sector.

As usual, incumbents like Intel are confident that we'll be using what they are offering. But as usual, the truth will be somewhat different (p32).

Watch the news In a survey (p30) we found that our readers are already investing in the Edge - even while they grapple with the questions above.

We'll be watching the arrival of Edge in reality, and we know you will be there too.

Edge Supplement 21

Peter Judge Global Editor

F

or the last 20 years, digital infrastructure has been centralizing, and the results have been incredible. Resources in large shared data centers are massively more efficient than those in small server rooms - and therefore massively cheaper. As a result, we now have access to an undreamed of wealth of online services, essentially for nothing.

But in the last few years, another strand of digital infrastructure has emerged. New applications, including streaming media, the Internet of Things, virtual reality and connected cars, will require large amounts of data delivered with very low latency.

For this reason, we are told, the world needs a new layer of infrastructure - Edge resources, close to the end user or devices they serve. The industry has got excited about this, with Gartner analysts predicting that enterprise data processed at the Edge will surge in the next four years, from 10 percent today, to 75 percent.

In response, vendors have stepped up: their main proposal to deliver the Edge is through modular “micro” data centers, lockable cabinets holding a single traditional rack or half a rack, complete with its own power and cooling.

But do the costs stack up? The trend towards centralization was driven by the economies of scale, which made webscale data centers cheaper to use. Edge will push applications back out to resources which must, surely, be more expensive. Edge applications must surely end up paying more for resources At this stage, Edge economics are a series of informed guesses, but Duncan Clubb, director of IT consulting at CBRE, agrees that “Edge facilities will naturally be more expensive than the ‘traditional’ cloud or colo services.”

Schneider Electric disagrees, claiming that that Edge resources can actually be cheaper than centralized cloud services.

“A centralized 1MW data center is $6.98/ watt and a distributed micro data center is $4.05/watt,” according to a White Paper, Cost Benefit Analysis of Edge Micro Data Center Deployments.

22 DCD Supplement • datacenterdynamics.com

The paper compares the capital expenditure (capex) of two alternatives: a “traditional” single data center with 200 5kW racks in a hot-aisle arrangement, and an Edge arrangement where the racks are installed in a set of micro data centers in different buildings, each one containing a single 5kW rack.

The Edge option comes out cheaper partly because micro data centers can be deployed cheaply in conventional office space, where power provision and real estate are sunk cost, while centralized data centers need more capital expense.

“This analysis is still somewhat hypothetical,” admits Victor Avelar, director of Schneider’s Data Center Science Center, which produced the paper. “However, I stand by the fact that when you locate a micro data center in an existing building, there’s infrastructure that you get ‘for free’ because you don’t need to build the building, the electrical distribution, generator, lighting, etc.”

Edge can come out cheaper in office space, where power and real estate are a sunk cost

Edge Supplement | Economics of the Edge

The cost of the Edge Resources for Edge applications will be more costly than centralized cloud capacity.

The applications will have to justify that higher cost, reports Peter Judge

On one level, Avelar says the analysis could overestimate the savings from shifting edge into micro data centers. The study assumed that micro data centers would not have 2N redundancy in their power and cooling, because in practice they generally don’t. In order to have a fair comparison, it also used a 1N specification for the centralized facility, which in reality would always have some level of redundancy.

However, there are some issues with the study. Firstly, it used a “traditional” centralized data center, so it will have missed the economies of scale that hyperscalers get from novel architecture in even bigger facilities.

Secondly, it does not cover running costs and operational expenditure (opex).

Centralized facilities make significant savings here. Cloud data centers are built where electricity can be bought in bulk at a favorable rate, while micro data centers have to accept the local electricity rate that applies in their building.

Cloud data centers also consolidate IT loads and storage, so the amount of hardware needed to run a given application would be less in a centralized site.

There’s another extra component of Edge costs, and that is management. “If we

really have mass produced edge computing, that is everywhere, there are not enough people to have dedicated facilities guys, managing these operations,” said Suvojit Ghosh, managing director of the Computing Infrastructure Research Centre at McMaster University, at DCD>New York. “It’s not a matter of cost. You can’t have one person per site, because there aren’t enough people.”

Edge facilities are therefore designed to be as autonomous as possible, and remotely manageable. Software is monitored and updated remotely, and hardware fixes are done by untrained stuff installing kit sent out by post. But there will still be an overhead in the cost and time of sending and managing these virtual and physical updates.

Set against these points, Avelar reminds us that Edge applications have specific communications needs. Placing them centrally will hurt them: as well as increased latency, they will have a higher cost to communicate to end users and devices.

This is a good point: Edge applications are different to traditional ones. Practically, they may do more communications than other applications, and their latency demands may be absolute. For instance, virtual reality goggles must respond to a change in the user’s gaze in 10ms or less, and self-driving cars must obviously respond just as quickly to avoid obstacles.

This doesn’t just affect where the apps are loaded, but also how they are structured, because part of that latency is in the operation of the application itself.

“Applications that require low latency need to be ‘always on’,” points out Clubb. “Low latency needs software to be loaded in memory and available for immediate response. Latency is destroyed by context switching, swapping or loading activities, in which the compute resource has to go and load up software into the processor from somewhere else in order to respond to incoming data.”

This means that, wherever they are operating, Edge applications have to be in high-grade computing space, says Clubb: “Ideally in a core and its memory, not sitting on a disk or in the wrong type of memory ready to be swapped in on demand.”

Avelar divides Edge workloads according to whether they are compute-intensive or storage-intensive. Compute-intensive applications have a latency limit of around 13ms, while storage-intensive applications vary depending on how the data is replicated.

If real-time replication is needed, they demand 10ms of latency.

These needs affect the distance from the IT resource to the end user or device, says Avelar. Storage-intensive workloads must be within 100km (60 miles) of their consumers, while compute intensive loads can be 200km to 300km (120 to 180 miles).

Both these figures are higher than the pictures usually conjured of Edge deployments. For instance, a regularly-cited model would place an Edge micro data center at every cell tower. While a cell tower has a maximum range of 70km (45 miles), they are typically spaced 2-3 km (1-2 miles) apart in populated areas. They are even closer together in cities, and likely to become even closer as 5G radio technologies arrive, with a much shorter signal range.

The picture becomes more complex when you consider that applications aren’t monolithic. Developers are likely to make sensible decisions about different parts of the code, says Clubb. “In practice, I expect to see the developers and owners of apps that will use the low latency aspects of 5G to split out the low latency code and deploy only the smallest necessary functions into Edge DCs, with normal cloud or data centers providing the majority of data processing and storage.”

Higher-cost Edge resources may end up running just ten percent of the overall Edge application, with 90 percent of it still running in a remote back end on an optimized virtualized server, using low-cost electricity.

Ghosh says: “I don't think the cloud is going anywhere. We always need that compute in the background to process data that doesn't have the latency requirements and does require economies of scale.”

Edge applications will be priced at a premium, Ghosh says, though he predicts that this premium will reduce as hardware evolves to meet Edge needs better.

For now, however, Clubb predicts there's a need to deal with reality: “Edge compute infrastructure will be expensive, well spec’d and with huge amounts of memory. Pricing models will be distinctly more expensive than normal cloud instances.”

This realization will weigh on the minds of those planning to roll out the first wave of Edge. Before Edge can truly take off, their applications will have to find a way to quantify that cost, and adopt a business model that justifies it.

Edge Supplement 23

At the edge of space When fiber and cellular coverage let you down, the answer could lie in a higher power, reports Sebastian Moss

W

e’re used to thinking of the Edge as close to us. We see it as nearby and low-latency, with devices distributed across dense cities, found in our shops, embedded in our vehicles and nestled in our homes.

But the Edge is much larger than that.

It stretches across the globe, with Internet of Things (IoT) devices inhabiting distant corners of the planet, cut off from any form of terrestrial connectivity.

“Even today, if you look at the coverage of LTE and 4G - as well as 5G in the future - only about 20 percent of the landmass of the world is covered by those technologies,” Tim Last, VP & GM of IoT at satellite communications company Iridium, told DCD. With some 80 percent uncovered, what is an IoT system to do if it has to connect to the wider world? To solve this challenge of connecting something remote, the solution lies even further away - in space.

“We're able to provide a fully global service, literally to any spot on the planet,” Last said, with sensors from mining, maritime, agritech and transportation all using Iridium.

"IoT is about a fifth of our business, but it’s the fastest growing segment. For six of the

last seven years we've added more customers and more revenue each year through IoT services than we have on every other product on the Iridium network.”

The company, which is just finishing its ambitious $3bn satellite refresh, serves some 678,000 active IoT devices, up 26 percent year-over-year. “We've been doing IoT for about 12 years now and have about 400 partners that have developed different kinds of applications that use that technology and service different kinds of markets.”

But the satellite-IoT market, like the wider Edge market, is changing as more devices become connected, and more businesses move to the cloud.

“We’re about to formally launch CloudConnect, a product that we developed in collaboration with Amazon Web Services,” Last said. “They're the market leader and they happen to already have customers in their traditional IoT and cloud services, who expressed the need to connect remote assets.”

Rival satellite communications company Inmarsat has also embraced the cloud, offering Industrial IoT services via Microsoft Azure.

"This gives Microsoft some capability to plug the gaps in their Azure network where

24 DCD Supplement • datacenterdynamics.com

Sebastian Moss Deputy Editor

their customers cannot get direct access to cloud,” Tara MacLachlan, VP of Industrial IoT at Inmarsat, told DCD. Both Iridium, with its 66 active satellites in low Earth orbit, and Inmarsat with its 13 satellites in geostationary orbit, offer various connectivity solutions that cost different amounts and require different hardware.

“Most of our IoT operates on short burst data (SPD), which is - as the name suggests - a sort of packet-based data-only service that operates over the lowest cost modems, transceivers that we're able to produce on our network,” Last said.

“You're talking in the region of a handful of kilobytes to maybe 100 kilobytes of data a month using our short burst data network.

When people hear me talking about kilobytes of data, sometimes they go ‘Oh my God, surely it's all about big data; surely it's all about megabytes and gigabytes?’ Well, the vast majority of IoT applications operate with relatively small amounts of data.”

Even then, with the cost of data transfer and of the devices required to send the data, it is important to make efforts to only send exactly what is needed.

“Critically, from Inmarsat's perspective, it's about bringing that data together at an intelligent Edge point,” MacLachlan said. “It’s

Edge Supplement | Edge of Space

about really interrogating the data coming in, understanding what data points need to be transmitted and what doesn’t,” using a software-defined Intelligent Edge Gateway to pre-process data.

“This gateway tends to be a very small footprint piece of hardware,” MacLachlan said. “The hardware supports all of that Edge intelligence and the connection,” generally using connection types such as Low Range Wide Area Network (LoRaWAN), Bluetooth Low Energy (BLE) and Ethernet to connect to the multiple Edge devices and IoT sensors in an area, sending back only a small amount.

"Some point of aggregation on the ground makes sense for those companies," Dr Alex Grant, CEO of startup Myriota, told DCD. "That kind of satellite technology is more expensive, the hardware is more expensive, and it typically requires more power."

His company hopes to offer a cheaper alternative - a small system that can last a decade on a single set of batteries, and can send data straight to the satellite. "That direct-to-orbit architecture is nice, because you then don't have an on ground network planning exercise to do," Grant said.

The company uses four satellites from maritime data services provider and Myriota investor exactEarth, with the aim of connecting millions of small systems that send tiny amounts of data. Grant said: "We can send data starting at as low as 20 bytes.

It's quite granular - if you just need to send a

very short message, you can do that using a very, very low amount of battery power and away you go."

Currently ramping from pilot tests to full commercial deployments, Myriota hopes to address a wider range of smaller IoT systems that Iridium and Inmarsat's more heavy-duty networks may be too expensive to use.

"You have different technologies for different use cases," Grant said, envisioning a scenario where farmers may want to “track a herd of 50,000 cattle, with a tiny little thing on each one.”

see a huge amount of consolidation, you're going to see half these players drop out and just disappear, you're going to see half of the rest consolidate. And then you might see one or two of them partner up with people. We've signed a couple of agreements,” he added, citing an MoU with Hiber and a service agreement with Fleet Space.

With a new satellite network and a growing business, Last is confident about Iridium’s future.

"I mean, that's not the case for many of our competitors, Inmarsat have virtually had to take themselves private in order to get out of the spotlight because their results have been pretty crappy.

“Globalstar is trying to sell itself for the spectrum, Orbcomm has basically gone into the cellular business because they decided they didn't want to do satellite much more.”

These fighting words come as space, like the Edge, is set to become a lot more crowded, pushing various untested business models to their limit. Last said: “Satellite's tough, but if you've got the right set of people and the right vision and strategy, you can make it work."

"It's about bringing that data together at an intelligent Edge point"

With the cost to access space falling rapidly thanks to SpaceX and the rise of smallsats, a number of newcomers are racing to compete for the burgeoning IoT market, hoping to undercut the incumbent providers.

"These kinds of networks are a good fit for the very low cost use cases and the customers that don't actually need to pay for low latency," Last said.

But he remains unconvinced that all the startups will survive: "I think you're going to

Edge Supplement 25

Advertorial: Schneider Electric

Successful Edge Computing IT Deployments in the New World of 'Compute Everywhere' Why edge computing capacity is needed… and how it needs to be designed, deployed and managed

I

t was not long ago when there was a pervasive feeling that everything was moving to the Cloud, an unstoppable, inevitable flow of on-premise IT capacity towards huge, highly centralized service provider data centers. But for many reasons, this hasn’t happened. One big reason has to do with the growing need for compute capacity at the edge of networks, closer to users, be it humans or other machines.

Why is this needed? Sending data over a network takes time and costs money…enough so that it is making more sense to deploy compute and storage at the edge rather than have all that data move to and from the cloud or some other remote data center.

The “Internet of Things” (IoT) phenomenon itself is a big driver for needing edge computing capacity as more and more connected devices are generating and collecting data. With the cost of sensors and network connectivity being so low, the volumes of data collected by local devices have exploded. Combine that with advances made in data analytics, automation, and artificial intelligence, and this data has become very valuable.

The increasing value of device data is driving this IoT world where everything is connected and continuously collecting data. It’s now making more financial sense to process, clean, and store at least some of this data locally than it would be to send it all to a distant data center.

Regardless of sensitivity to latency and cost, the fundamental driver for having compute at the edge of the network really comes down to the desire to benefit from digitization. Adding compute and network

connectivity to every “thing” and to virtually every aspect of our society is dramatically impacting society’s productivity, efficiency and wellbeing. Compute everywhere is our future.

Use cases for edge computing deployments have exploded as a result.

The digitization of industrial processes and manufacturing is certainly a key use case.

Brick and mortar retail’s deployment of local IT for providing in-store, immersive digital experiences is another.

Deployment of 5G mobile networks, however, may have the biggest impact on the growth of the edge computing market. 5G offers the promise of submillisecond latency, a speed necessary to make many of the world’s tech dreams a reality such as fully autonomous vehicles, robotic surgeries, virtual/augmented reality, and the real-time management of distributed energy sources. 5G will enable a world of incredibly high data speeds for huge numbers of users, while improving reliability and security in an energy efficient manner. 5G’s communication architecture requires the deployment of hundreds of thousands of mini communication clouds and antennae to make all of this come to fruition. So not only will 5G networks help drive the larger edge computing market by enabling edge applications to do even more, the deployment of 5G itself will be a significant edge computing application driving the overall market.

This “compute everywhere” trend has led us to a hybrid computing architecture where more and more organizations’ IT assets and data are spread across large centralized data centers, smaller regional

5G’s communication architecture requires the deployment of hundreds of thousands of mini communication clouds and antennae to make all of this come to fruition data centers and very small local edge sites. This highly distributed environment creates challenges for those deploying and managing the IT infrastructure. And this complexity is exacerbated when you consider that each of the local edge sites require high availability to ensure uninterrupted operations and service. As IoT technologies and edge computing applications become a more integral part of the day-to-day business and/or customer experience, the edge IT infrastructure that houses the associated distributed IT equipment must be robust. The role of IT is no longer viewed as a cost center, rather it is tightly connected to the business strategy and to profit as a value creator, making resiliency even more imperative.

There are two unique attributes of local edge environments, in contrast with regional edge or centralized data centers, that make it challenging to achieve the necessary resiliency: (1) lack of IT and/or facilities staff on-site, and (2) having many sites, geographically dispersed.

Advertorial: Schneider Electric

These two things create issues such as: • Complexity in selecting and configuring IT infrastructure components: mistakes can propagate across sites, you must choose parts that not only support the application but that also are compatible with the other parts and with each site’s local conditions • Logistical/workforce challenges of deploying IT infrastructure to each site • Operating and maintaining multiple sites from afar: having visibility to all sites and assets, troubleshooting problems without local trained staff, maintaining everything in a standardized efficient way, interpreting alarms and status notifications, knowing who is accessing the equipment, and so on Mitigating these challenges starts with partnering with a collaborative ecosystem of vendors, integrators, and service providers. Together using their expertise and tools, they can address configuration, integration, delivery, and operations/ maintenance challenges. IT vendors should drive easy interoperability through reference designs, selectors, APIs, and certification programs. Effective physical infrastructure vendors (racks, UPS, cooling, etc) simplify deployment & operations through easy-to-use configurators,

reference designs, resilient infrastructure, and software management tools. Systems integrators will add tremendous value through the complete integration of your IT hardware, software, and physical infrastructure systems ideally before delivery to the site. And, finally, managed service providers (MSPs) will operate & maintain edge infrastructure for you through management tools and digital services provided either by themselves or by the infrastructure vendor.

Software management is a critical part of the solution to solve edge challenges.

The tools are necessary for giving visibility and control from afar. New cloud-based software suites have emerged that offer open APIs and take advantage of cloud, IoT, data analytics, and artificial intelligence

technologies. These new tools are what connect members of the ecosystem together to the operations phase of edge IT deployments. These new capabilities along with the MSPs who employ them essentially augment staffing for the end user by providing remote visibility and proactive control over all edge IT assets.

To paint the picture in very broad strokes, the ecosystem works together to simplify the design and deployment phases while providing both a physical and virtual workforce to ease management and maintenance burdens. By working together, edge computing owners and operators will be capable of not just surviving in our new complex world of “compute everywhere” but should be well-positioned to thrive in whatever ways they serve their customers.

Schneider Electric Roseann Brusco 401 363 7058 roseann.brusco@se.com

The edge of things The Internet of Things promises to connect billions of objects. James Pearce explores the Edge resources that will require

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ut on the Edge, you see all the kinds of things you can’t see from the center,” said writer Kurt Vonnegut. We’re sorry Kurt: we know you were talking about taking risks in real life, but if we utterly misread this quote, it could be about the difficulty of managing the Internet of Things from a centralized resource.

Every day, 2.5 quintillion bytes of data are processed, and by 2020, it's estimated that 1.7MB of data will be created every second for every one of the 7.5 billion people on Earth. Those remarkable figures ignore the Internet of Things (IoT), which promises to connect vast raft of devices in the next few years, with industry figures estimating there could be as many as 30 billion connected devices in the global ecosystem by 2025.

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James Pearce Contributor

To support the IoT, technologists are looking to Edge computing as a way of processing this flood of data in a timely way.

As endpoint devices become producers of data instead of consumers of it, who wants to send that data all the way back through a narrow pipe to a data center for processing?

According to AFCOM’s State of the Data Center Industry study, Edge solutions are one of the top areas of focus for data center

Edge Supplement | The Internet of Things end users, with 44 percent having already deployed some form of Edge computing capacity or planning to do so this year.

Another 17 percent say they have Edge computing plans in their pipelines during the next three years.

Frederik Forslund, VP of enterprise and cloud erasure solution at data security firm Blancco, claims this interest is being driven by growing demand for connectivity and mobility. “Edge computing will process data to facilitate services as close to the user as possible. This will enable better service quality for selected applications and services - addressing the increasing demand for data, without necessarily increasing data traffic. Edge computing therefore also improves service availability and connectivity. This will be paramount in enabling automotive IoT applications, augmented and virtual reality services.”

Here’s a problem though. Most of our IT is now done in centralized facilities, like cloud data centers, and these aren’t a great fit for IoT data, for various reasons.

If the IoT data originates with people, there’s a risk of privacy and security breaches when it passes to central facilities.

Many IoT applications, such as real-time control, have to have a quick response.

Centralized services increase the latency, and can harm these applications.

Cloud costs can be high, if large volumes are stored there.

Large amounts of IoT data can clog networks, and increase network costs.

IoT applications often create large amounts of data, much of which can be discarded once patterns have been extracted.

For this reason, the last two problems can be alleviated by local processing.

Leppard agrees: “Decisions will need to be made about what compute will happen at the Edge location and what can be achieved back at base. These data transactions will put new stresses on the connectivity between tens, hundreds or thousands of Edge sites and the enterprise hub or hubs supporting the network.”

By transferring more of the high level processing to the Edge, he adds, IoT devices can be made smaller, lighter and cheaper, providing an “almost an unlimited opportunity to make our world connected, smart and digital.”

All of this leads to a conclusion: the IoT needs local or Edge processing. The data center sector’s response has been to promote smaller and more modular “micro data centers.” Research from Global Market Insights predicts that the micro data center market will surpass $14.5 billion by 2025, seeing a compound annual growth rate of over 25 percent.

Massimo Bandinelli, cloud and data center marketing manager at European hosting firm Aruba.it, says IoT and other Edge demands “will lead to a significant change in the data center landscape by driving an increase in smaller data centers and Edge-driven systems that will work alongside existing data centers."

Will Edge computing have an impact on businesses set up for centralized processing? Bandinelli says: “The traditional data center model will still thrive and be vital to all kinds of businesses as it retains its role

"Edge computing will process data to facilitate services as close to the user as possible"

Some IoT hype is overstated. Consider this: of those 30 billion IoT devices, many will be fridges, smart speakers or TVs that can be used in the home. For these devices, instant compute may not be necessary. For your fridge, for example, connectivity is quite simple – hook it up to a WiFi connection and the fridge can send data back to a Samsung data center, or directly to your device, whenever (or if ever) you need to use the connectivity functions on the device.

Other IoT devices such as temperature sensors may only produce small packets of data, which can often be queued without danger.

But a significant number of connected ‘Things’ will need lower latencies. If those temperature sensors are safety critical, the data has to get through quickly. Vast numbers of these devices can cause fluctuations in data volumes, creating a difficulty in delivering fast services to them.

That’s why so many companies are getting involved in the IoT Edge. HPE in June talked up its “Cloud-to-Edge” strategy, unveiling plans to spend $4bn over the next four years on AI, ML, and Edge computing products and services that will find their way into IoT solutions.

Edge computing will have a “massive impact” on the global connectivity landscape, according to Jonathan Leppard from Future Facilities, a data center software and simulation company.

at the center of these new networks and is essential for processing the core data that requires long-term retention.”

In fact, traditional colocation players with facilities near their customers can also play at the IoT Edge, as some Edge applications can in fact be 300km from their users (see article “The cost of the Edge,” p22).

The more facilities they have, the more convincing this is. Take Equinix: With 200 data centers across 52 markets, it can feel that you are never far from an Equinix data center, which lends strength to its Edge pitch.

“Equinix has facilitated ‘things’ communicating in a machine-to-machine manner over private networks for quite a long time,” says Russell Poole, managing director at Equinix UK. “We are seeing the need for significant Edge computing capabilities, especially when real time decisions are needed on the basis of IoT data, driverless cars being the obvious example we all use.”

Another colocation player with a long-standing interest in the Edge is EdgeConneX, with 40 data centers across more than 30 markets. Phillip Maragella, CMO at EdgeConneX, says: “The massive growth in the volume and velocity of data (boosted by IoT) traversing the Internet will require a more distributed architecture that requires an expansion of both near Edge and extension of far Edge data center capacity to handle this tsunami of data that will be flowing back and forth.”

More intelligent IoT devices will still need management and orchestration from within traditional data centers, and the data center firms may develop Edge services delivered along traditional “cloud” lines, says Cambridge Consultants head of connectivity Robert Milner: “The most significant opportunity for data centers is in developing new business models to embrace advances in Edge computing. We may see commercial strategies emerge that switch the ownership models around or are closer to traditional cloud services, where data centers offer compute and logic closer to end-users.”

IoT isn’t just temperature sensors either, says Equinix’s Poole. Global law firm HFW is using a digital Edge to improve the customer experience in key markets such as Dubai, London, Hong Kong and Paris: “HFW has distributed its IT to be closer to customers and employees.”

IoT is still emerging, but EdgeConneX’s Marangell sees an opportunity: “The major players in the space are also the major cloud providers. For us, it’s all about providing them and the rest of the IoT ecosystem an end to end data center solution from hyperlocal to hyperscale.”

Edge Supplement 29

Reality Check Edgy attitudes from our readers

We asked readers for their opinion on the current and future state of Edge technologies. Will Calvert presents the results

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hen we invited response from readers to a survey on Edge technology and implementations, companies from all over the world responded, including people at IBM, Amazon, Schneider Electric, NTT Singapore, Vertiv, Furukawa Electric and many more.

Amongst those that were kind enough to fill in the survey, it seems that most work in manufacturing (29 percent), followed closely by the traditional data center field of colocation and multi-tenant data center (MTDC) services (25.6 percent). The remaining 46 percent of responses were split across cloud services, financial services, telco/ISP services, retail and other verticals.

Edge is going to happen, according to the survey: 79 percent of respondents think it

will become as important as traditional data center infrastructure. One reader responded to this question saying that Edge technology will be “critical, but not as profitable for firms used to making/selling the big equipment in colocation sites.” This opinion was echoed by others, who said that some of the bottlenecks they foresaw with both Edge and 5G technologies were: “Cost and the current trade war,” “cost and innovation” and “time and cost for investment.”

Is the industry ready for Edge and 5G? You are evenly divided. Roughly half (54.6 percent) said 'Yes 'and the remaining 45.3 percent said 'No.' Overall, there was a somewhat positive consensus on Edge/5G, but several of the comments reflected a more negative take. One person said: “There is a lack of

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Will Calvert Reporter

technically skilled employees to do the work.”

Others were worried about “physical security, resilience, and redundancy vs cost,“ and that “German law will hamper the rollout of 5G massively. Rollouts to be finished by 2025 will not occur.”

We found that on average, respondents spent 26 percent of their current annual budget on Edge technologies, but the spread ranged from those spending absolutely nothing on Edge, all the way to others claiming they are spending 100 percent of their budget on the technology.

Digging a little deeper, it seems the technology is still emerging. One quarter of those who answered are spending between one and ten percent of their budget on Edge.

The next most popular option was between 11 to 20 percent, which made up 15 percent of the answers.

Other

Edge Supplement

No Yes

Nearly 80 percent agreed that Edge will become as important as traditional data center infrastructure, but that optimism was not reflected in the budget: the average spend predicted for Edge in five years’ time, only increased by nine percent. In this projected budget, only one person said they will not be spending anything on the Edge in five years’ time, compared to the 13 people (15 percent) who said they do not currently spend anything on the Edge.

When asked for words to describe Edge technologies the most frequent response was ‘necessary.’ The second most used word was ‘progressive’.

Given the option to supply their own words, respondents suggested terms including ‘decentralized’, ‘mis-marketed’ and ‘smaller (physically)’.

No

Yes

A commenter asked for a more practical solution to help implement the Edge: “It would be a great help if all the technology providers set a standard integration with all their networks.”

One reader was concerned about what Edge means for traditional IT as a whole: “The Edge is the last bastion for traditional IT providers before public cloud providers ultimately come for that too.”

Not all the opinions were so negative: “Edge is very easy to work with and I am happier to work with Edge technologies than I am with traditional data center technology.”

One person asked, “what is an Edge technology?” while simultaneously claiming to be spending 20 percent of their annual budget on Edge technologies. We can only hope that this respondent finds our Edge supplement enlightening.

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Processing the Edge The future of computing will be spread across the network - from the device, to the Edge compute node, to the data center. Sebastian Moss investigates who will process the Edge

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e're soon going to hit a crossover point where there will be more computing power at the Edge than in data centers or the cloud, according to Justin Boitano, Nvidia’s senior director of enterprise and Edge computing.

His prediction is shared by many in an industry preparing for a world where sensors proliferate, Internet of Things systems populate our cities, and the Edge rules.

But, with no single computing architecture dominating this growing field, this shift means there is a huge market potentially up for grabs - and everyone wants a piece of it. There’s a fight going on, between the leading chip designers, cloud giants, and plucky startups, all of which want to build the processors that will run the Edge.

Intel is one such contender: With the company’s CPUs dominating the data center, it hopes to translate that success to the Edge.

"What we've done with the second generation Xeon Scalable Processor is put features in there that make it really ideally suited for processing at the Edge, and the Edge could be either close to the devices themselves, or the Edge of the network," Intel senior principal engineer Ken Shoemaker said.

Senior network engineer Tim Verrall linked this to the telephone network, whose exchanges (often referred to as “central offices”) are important in many Edge network proposals. They serve as convergence points for local telephone systems (the “local loop”) and the telecommunications providers’ long-haul backbone networks, and have preexisting power and cooling infrastructure.

Next generation processors are “ideal for the next generation central office, which is in the early stages of deployment,” said Verrall.

“There's a large number of central offices, and they are typically within about 10 miles of

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Sebastian Moss Deputy Editor

the end point - that's where the Edge is being deployed today."

Verrall continued: “The amount of traffic that 5G is likely to put on the network is really going to force services to be offered at the edge and the telecoms providers are going to require this offloading of data. Otherwise, their backbone networks are going to be overwhelmed.”

But a local loop of up to ten miles may place the central office too far from the edge for some applications: Self-driving cars, for example, cannot afford any latency, and companies like Renesas, NXP, Tesla and Intel are competing to develop hardware that runs in the vehicle itself.

For other connected Edge devices, like security cameras, investing in on-device hardware that does some pre-processing yields savings. “If you think about that camera - say it is focused on a door, 99 percent of the time the door is closed, so that video sensor can make the assessment that

Edge Supplement | Processing the Edge the door is closed, nobody's walking in or out, and you don't have to send any data back,” Mohamed Awad, Arm's VP of infrastructure business, told DCD. “At some point, the door opens, and somebody walks through the door, and the video sensor may distinguish that this isn't a person that's supposed to walk through the door, and therefore wants to send it back to a mobile Edge computer, which then does some facial recognition to determine who it is,” Awad explained. “And then it sends that data up to the cloud, and the cloud does some further analysis.”

Awad sees it as an end-to-end system, where “heavy compute is going to run closer towards the core, towards the data center, while the lighter compute is going to run more towards the Edge where there's more sensitivity around power and cost and all that kind of stuff.”

Arm, while it has struggled in the server CPU space, has a huge footprint at the Edge: its chip designs are found in more than 130 billion products, including essentially all smartphones. Its owner, Japanese telecoms giant SoftBank, sees a much larger market ahead - touting the lofty goal of one trillion devices. "It's not that far off," Awad said.

won't bear it, your storage won't bear it.

“You need to do AI at the Edge, to focus on what to react to or send back." El-Ouazzane, who was CEO of low-power computer vision chip company Movidius prior to its acquisition by Intel, again sees the Edge as spread across various layers, from the device, to an aggregation point, to perhaps an Edge server, and then the data center.

“You're dealing with different power envelopes depending on if it's an end device or an aggregation point. When it comes to devices, for AI acceleration, the power envelope is sitting anywhere between a matter of milliwatts, up to three watts. When you’re looking at aggregation points, you're getting between 10 watts of power dissipation up to 50 watts.”

Intel and Nvidia are far from alone in targeting the Edge AI market, with a bevvy of startups hoping that this new front in the AI chip market provides an opening.

"I'm focused on the Edge," Orr Danon, CEO of Israeli chip company Hailo, told DCD. "Most processing will happen at the Edge, where there's much more data that you want to digest into simpler representations."

Fresh off of a $20m funding round, the small company hopes that its Hailo-8 processor will end up in everything from security cameras to autonomous vehicles, drones and more.

Hailo's 26 teraops (26 trillion operations per second) chip consumes almost 20 times less power than Nvidia’s Xavier AGX on ResNet-50 benchmarks, the company claims.

"We look at things that are from milliwatts to a few watts, we're not looking at things that are a hundred to 1,000 watts," Danon said.

Facing goliaths like Nvidia, Intel and Arm with their huge teams and giant warchests, it is tempting to dismiss new approaches like Hailo. Danon, unsurprisingly, disagrees: "If you look from a historical perspective, every time there was a big shift in computers or the purpose for which computers are used, a huge opportunity was created. And the winners were never the established players never,” he said, highlighting how IBM failed to move on from mainframes, and how Intel failed to capitalize on the rise of mobile.

Danon believes that “when you're looking at an evolution of architectures, the player with the most resources and the market access always wins, but when you're looking

"Most processing will happen at the Edge, where there's much more data that you want to digest"

This explosion at the Edge is coming at the same time as another massive transformational change: Artificial intelligence. Take the security cameras - each could use AI processing to filter out unnecessary video data and highlight relevant anomalies, something Nvidia hopes to cater for with its new EGX Platform, a reference architecture coming in various sizes, from the tiny Jetson Nano, up to a full rack of T4 servers.

“Depending on how many cameras you're trying to analyze, there's going to be a range of hardware solutions under our EGX platform,” Boitano said. “An AI micro server running Azure IoT can process about four cameras in the small form factors, and then a full rack can process up to 3,000 cameras.”

Intel's VP and COO of its AI Products group, Remi El-Ouazzane, sees a similar market opportunity for his company: "The biggest problem with vision workloads is bandwidth, especially in the surveillance space. If you're sending 4K or 8K video, 30 frames per second to your system, and you deploy thousands of cameras - your network

at a revolution it actually goes the other way around. Your legacy and commitments slow you down,” he said, citing examples like Google versus Yahoo, and Facebook versus Google.

“So the question - and I think that will determine who will win - is how big of a revolution is deep learning at the Edge? How deeply does it represent a change in the compute paradigm than what we have had?”

Arm’s Awad also foresees a revolution, one which will open the market to various forms of computing architecture. “I mean, listen. We have an architecture, so certainly we want a unified architecture, and we want it to be ours. But we're realists about it, no one architecture is going to be able to solve all of the problems of dealing with a trillion devices. It's going to require lots of different types of compute that exists in lots of different places with lots of different profiles.

And that's okay.”

If you accept this new computing world of various architectures processing various aspects of data between the Edge point and the data center, “then you can start to free yourself from this notion of, 'Hey, I'm going to develop for a particular architecture that's going to exist in a specific place,' and you can start thinking about things more around, 'hey, I want to just develop my workload,’” Awad said.

“This is what developers tell us, they say ‘I just want to develop my application, and I just want somebody to give me a servicelevel agreement which says that I'm going to get a certain amount of bandwidth, a certain number of compute cycles, and a certain amount of latency.' Where that workload actually exists, what piece of hardware it's sitting on, doesn't actually matter that much.”

Edge Supplement 33

Edge increases the attack surface

Dan Swing Contributor

As well as potentially unique challenges around physical security, Edge locations require greater considerations around data security, says Dan Swing

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he Edge data center market is on the edge of booming. The market is expected to exceed $13 billion by 2024 according to a Market Study Report.

The mean number of Edge locations is predicted to reach 12 within three years, up from six today.

While the number of sites is growing, so is the amount of data they are gathering,

with Gartner predicting the majority of enterprise data will be handled there by 2025.

The Edge is becoming more prevalent because of use cases such as pre-processing of Internet of Things data (p28) before it is sent on to the host data center or as lowlatency hubs for content distribution. But all this growth is making Edge facilities prime targets for threat actors.

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Edge data centers pose all the same challenges around security and resilience as a traditional data center. However, they also pose new and unique challenges due to their placement, environments, and use cases.

You cannot take Edge security for granted, and assuming you have the kind of security you might see in a centralized data center (whether it has Uptime Tier certification or not) may lead to pitfalls.

Edge Supplement | Edge Security Location risks Edge data centers can be located in a huge variety of places, including self-reliant processing hubs attached to telco base stations, and micro data centers in branch offices or factories. A lot of the logical and physical controls available in larger data centers may not be available or practical in these locations, making a standardized approach to security across all your Edge locations difficult.

“Simply replicating an on-premise cyber security strategy at the Edge is impractical,” warns Stephen Marchewitz, director of risk management at security consulting company TrustedSec. “Those at the Edge will be unmanned as there are too many to have staff to be cost effective. Information about the status of the facilities will generally take place in the cloud, without the traditional command and control mechanisms that they are used to.

“The fact that Edge data centers will be unmanned increases risk as the time to resolution for an onsite problem will generally be longer. This is especially so if multiple Edge data centers go down at the same time. It also creates greater risk if orchestration, automation, and response are not planned out correctly and tested to ensure the processes are working.”

Not only could compromised Edge locations lead to data breaches of the information on the devices or potentially act as entry points to core networks, but threat actors could corrupt data being sent back and forth between the home network, the Edge location, and other devices feeding into that location. This could lead to incorrect information being sent back to the business, incorrect instructions being sent out to any device connected to the Edge data center, or potentially have it used as a part of distributed-denial-of-service (DDoS) attacks.

“Essentially the fundamental cyber security challenges haven’t changed because of the introduction of Edge centers,” says Gary Criddle, cyber risk and business resilience consultant at Sungard Availability Services, “but the dispersed nature of the data and the use of numerous smaller data centers simply multiplies the number of touchpoints that attackers can play with.

“IoT connected devices already pose security issues and every IoT device connected to a network effectively becomes a doorway into that network. I’m sure we will learn the IoT security lessons the hard way and Edge computing will be at the epicenter of these problems when they hit.”

Physical security On the physical side, by and large Edge facilities should be secured in the same way as servers in branch locations or telecoms

base stations, with as many physical controls in place as is feasible. These include walls or fences where suitable with strong doors and locking systems. If located within an office or factory, sturdy enclosures with robust locking doors should be installed and the security procedures of the entire building should be assessed and updated if necessary. All servers and racks should be securely tied down to prevent unauthorized removal. Visible deterrents such as barbed wire and warning signage may deter spur of the moment attacks.

Give the greater number of locations, many of which may have little or no staff and may well be hours away from the nearest engineer. Limited infrastructure means facility monitoring becomes even more important. Access controls such as a keypad, keycard, or even biometrics system should be used (and logged), along with burglar alarms, 24-hour alarmed surveillance via CCTV, sound and motion detectors, as well as fire detection and suppression systems.

Additional detection mechanisms such as proximity, infrared, temperature, and even pressure sensors can provide a more holistic view of a location.

As physical security merges more with digital security, artificial intelligence is increasingly being deployed in efforts to better secure physical locations. Swedish ‘smart building’ research firm Memoori predicts AI-based video analytics could “dominate” physical security investment over the next five years. For example, CCTV-based image recognition can detect if people are present in view, meaning alerts can be set up if no one is scheduled to be on-site, behavioral analytics around access control systems can alert you to unusual or unexpected use of keycards at edge.

Qualys. “Strengthening visibility is crucial to understand what you have before being able to defend and protect it. This involves deploying specialized security sensors to observe what is installed in the Edge data center, organize and classify it, gain information about it and then stream this information to the central brain where it can be processed holistically.”

If used within IoT use cases, large volumes of devices connecting from random IP addresses to your location increases the complexity compared to more controlled environments. Increased monitoring – both in terms of data being sent back and forth as well as who is accessing terminals – and implementing a strict alerting system for abnormal traffic activity or unscheduled behavior if collecting information in batches will help flag potential issues.

“The ideal approach is to build security in from the start versus bolting it on as an afterthought once the Edge data center has been put together,” says Rottigni. “Best practices here include passive traffic listening, implementing container security, and building security system agents into any golden images for deployment or via SDK into IoT devices. All this data should also be integrated with cloud service providers APIs. Encryption becomes more critical because data travels over potentially more insecure channels.”

Encryption - both in transit and at rest is incredibly important to ensure that if any data is compromised it’s less likely to be used or abused by attackers. you must also plan how to scale all of your security activities to suit the larger footprint of Edge.

“Traditional data center encryption may have a limited number of “sessions” between assets whereby information is encrypted and decrypted,” says TrustedSec’s Marchewitz.

“This is different in that a much larger number of devices are connecting more often causing potential delays that the Edge data center was meant to relieve, and so device-to-data-center encryption needs to be able to be scalable to meet the increased demand for large number of devices connecting in a short period of time.”

“With the sheer number of devices leading to an ever-increasing number of Edge data centers required, poor planning up front on security, or any missteps will have domino effects as they are rolled out.”

“Simply replicating an on-premise cyber security strategy at the Edge is impractical”

Data security While physical security is important, the data security element is elevated in importance compared to other deployments purely because the information within those locations sits outside the traditionally wellknown confines of your network.

“In these ephemeral environments, visibility is the first challenge from a security standpoint,” says Marco Rottigni, chief technical security officer EMEA at

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Edge Supplement

Searching for the Edge on 5G 5G is supposed to be a key part of delivering Edge applications. But the biggest Edge application could be the 5G network itself, says Peter Judge

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ny talk of Edge inevitably comes round to 5G, the next generation mobile data network, that is due to be deployed over the next couple of years. 5G will offer increased data rates, that we are told will be needed by predicted Edge applications.

After years of preparations, 5G networks began to appear in April and May 2019. SK Telecom and others are offering it South Korea, Verizon and AT&T have launched in the US and EE has 5G in the UK. Other countries and networks will follow swiftly possibly by the time you read these words.

Early testers say the technology is living up to expectations. TechRadar clocked speeds of more than 1Gbps in the US, and around 500Mbps in the US, which is much faster than 4G networks.

Like previous generations, 5G upgrades the core networks run by the operators, and also the radio protocol by which they communicate to end users and devices. The new radio technology, helpfully called 5G NR (for “new radio”) could offer speeds up to 20Gbps, but that speed will be a largely theoretical one - much like the 2.4Gbps top speed that none of us get from today’s LTE networks.

5G NR operates in two frequency bands, one below 6GHz (normally 3.5GHz), and the other is in the mmWave band, between 30-300GHz. The lower frequency band most like existing networks, is most commonly deployed now, and offers a moderate improvement over 4G. The mmWave option is deployments more exotic and likely to give faster speeds, at the cost of a shorter range, which will require many more base stations to be deployed.

Again, like previous generations, the technology will come in gradually, using “non-standalone” mode to share the existing core network.

The media is asking about 5G handsets, hotspots, routers and other paraphernalia used by humans. The digital infrastructure industry will want to know: what can 5G do for Edge applications, like the IoT and autonomous vehicles?

For the Internet of Things, the important thing will be equipment to support machineto-machine communications for the IoT’s sensors - 75 billion of which are predicted to be hooked up by 2025. The standards support low power wide area (LPWA) networks, with options including NB-IoT (narrowband IOT) and LTE-M (LTE-machine type communication).

But these sit somewhat strangely in the 5G hype, as they were already defined for the previous 4G standards, and they deliberately use a lower speed than is being pushed as the major benefit for end users.

The fact is that the IoT may have billions of sensors, but they mostly don’t need high bandwidth. They tick along handing over small packets of temperature readings or other data, and mostly need a long battery life and good coverage. 5G can potentially offer the first, but it will be some while before it approaches the second. For a long time to come, the IoT will find 4G completely adequate.

Some hope that autonomous cars could be the “killer app” for 5G. Duncan Ellis, of data center IoT firm Wave2Wave says it would be too expensive for them to carry enough processing to be fully autonomous. Instead, they will need sub-millisecond responses from external pattern recognition to avoid

36 DCD Supplement • datacenterdynamics.com

Peter Judge Global Editor

obstacles such as pedestrians.

But even 5G includes delays, and the possibility of lost signals, so there’s a strong strand in autonomous vehicle work trying to compress more of what is needed into the vehicle, effectively pulling back from relying on 5G. Paradoxically, the application which will really stress 5G networks may be those human users we talked about earlier. They are pumping the same kind of data they always have, but they are doing it faster than ever before, and that may change things, “With constraints like energy utilization and latency, we can’t keep going back to a centralized data center infrastructure,” said Ellis in a recent DCD webcast. One user taking 1Gbps of data would effectively take the entire backhaul available in one of today’s cells.

Fiber to the cell towers might ease that bottleneck, but that will be expensive. For now, the network will have to get better at moving the data closer to users. “This represents a huge challenge for those building networks, because you have no way of knowing at any time where the data flow is going to,” says Ellis.

In the end, the biggest Edge application on the 5G network, could be the 5G network itself, and its human users.

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AI + Automation | AI & Data Centers

Smartening up Data centers are one of the first places where AI and machine learning can make a difference. Peter Judge reports

W

e have an almost mystical faith in the ability of artificial intelligence (AI) to understand and solve problems. It’s being applied across many areas of our daily lives and, as a result, the hardware to enable this is starting to populate our data centers.

Data centers in themselves present an array of complex problems, including optimization and prediction. So, how about using this miracle technology to improve our facilities?

Machine learning, and especially deep learning, can examine a large set of data, and find patterns within it that do not depend on the model that humans would use to understand and predict that data. It can also predict patterns that will repeat in the future.

Peter Judge Global Editor

Data centers are already well-instrumented, with sensors that provide a lot of real-time and historical data on IT performance and environmental factors. In 2016, Google hit the headlines when it applied AI to that data, in order to improve efficiency.

Google used DeepMind, the AI technology it owns, to optimize the cooling in its data centers. In 2014, the company announced that data center engineer Jim Gao was using the AI tech to implement a recommendation engine.

In 2016, the project optimized cooling at Google's Singapore facility, using a set of neural networks which learned how to predict future temperatures and provide suggestions to respond proactively,

Issue • July 2019 39 AI33 Supplement 39

h

The results shaved 40 percent off the site's cooling bill, and 15 percent off its PUE (power utilization effectiveness), according to Richard Evans, a research engineer at DeepMind. In 2016, he promised: “Because the algorithm is a general-purpose framework to understand complex dynamics, we plan to apply this to other challenges in the data center environment and beyond.”

The next step, announced in 2018, was to move closer to a self-driving data center cooling system, where the AI tweaks the data center’s operational settings - under human supervision. To make sure the system operated safely, the team constrained its operation, so the automatic system “only” saves 30 percent on the cooling bill.

The system takes a snapshot of the data center cooling system with thousands of sensors every five minutes, and feeds it into an AI system in the cloud. This predicts how potential actions will affect future energy consumption and picks the best option. This is sent to the data center, verified by the local control system, and then implemented.

The project team reported that the system had started to produce optimizations that were unexpected. Dan Fuenffinger, one of Google’s data center operators who has worked extensively alongside the system, remarked: "It was amazing to see the AI learn to take advantage of winter conditions and produce colder than normal water, which reduces the energy required for cooling within the data center. Rules don’t get better over time, but AI does."

According to Gao, the big win here was proving that the system operates safely, as well as efficiently. Decisions are vetted

against safety rules, and human operators can take over at any time.

At this stage, Google’s AI optimization has one customer: Google itself. But the idea has strong backing from academia.

Humans, and simple rule-based systems can respond to any steady-state situation, but when the environment changes, they react in a “choppy” way - and AI can do

It's bad to run servers too hot, but sudden changes in temperature can be even worse better, because it is able to predict changes, according to DCD keynote speaker Suvojit Ghosh, who heads up the Computing Infrastructure Research Centre (CIRC) at Ontario’s McMaster University.

“We know it's bad to run servers too hot.” said Ghosh. ”But it's apparently even worse if you have temperature fluctuations.” Simple rules take the data center quickly to the best steady state position, but in the process, they make sudden step changes in temperature, and it turns out that this wastes a lot of

40 DCD Magazine • datacenterdynamics.com

energy. If the conditions change often, then these energy losses can cancel out the gains.

“If you have an environment that goes from 70°F to 80°F (21-27°C) and back down, that really hurts," said Ghosh.

Companies in data center services are responding. Data center infrastructure management (DCIM) firms have added intelligence, and those already doing predictive analytics have added machine learning.

“The current machine learning aspects are at the initial data processing stage of the platform where raw data from sensors and meters is normalized, cleaned, validated and labeled prior to being fed into the predictive modeling engine,” said Zahl Limbuwala, cofounder of Romonet, an analytics company now owned by real estate firm CBRE.

The move for intelligence in power and cooling goes by different names. In China, Huawei’s bid to make power, cooling and DCIM smarter goes under the codenames iPower, iCooling and iManager.

Like Google and others, Huawei is starting with simple practical steps, like using pattern matching to control temperature and spot evidence of refrigerant leaks. In power systems, it’s working to identify and isolate faults using AI. In its Langfang data center, with 1,540 racks, Huawei has reduced PUE substantially using iCooling, according to senior marketing manager Zou Xiaoteng. The facility operates at around 6kW per rack with a 43 percent IT load rate.

DCIM vendor Nlyte nailed its colors firmly

th AI + Automation | AI & Data Centers to the DCIM mast in 2018, when it signed up to integrate its tools with one of the world’s highest profile AI projects, IBM’s Watson.

Launching the partnership at DCD>New York that year, Nlyte CEO Doug Sabella predicted that AI-enhanced DCIM would lead to great things: “The simple things are around preventive maintenance,” he told DCD. “But moving beyond predictive things, you’re really getting into workloads, and managing workloads. Think about it in terms of application performance management: today, you select where you’re going to place a workload based on a finite set of data. Do I put it in the public cloud, or in my private cloud? What are the attributes that help determine the location and infrastructure?

“There’s a whole set of critical information that’s not included in that determination, but from an AI standpoint, you can contribute into it to actually reduce your workloads and optimize your workloads and lower the risk of workload failure. There’s a whole set of AI play here that we see and our partner sees, that we’re working with on this, that is going to have a big impact.”

Amy Benett, North American marketing lead for IBM Watson IoT, saw another practical side: “Behold, a new member of the data center team, one that never takes a vacation or your lunch from the breakroom.”

DCD understands the partnership continues. The Watson brand has been somewhat tarnished by reports that it is not delivering as promised in more demanding areas such as healthcare. It's possible that this early brand leader has been oversold, but if so, data centers could be an arena to restore

its good name. The vital system of a data center is much more simple than the human body.

It's time for AI to reach for bigger problems, says Ghosh, echoing Sabella's point. After the initial hiccups, efforts to

After optimizing power and cooling, AI can start moving the IT loads themselves improve power and cooling efficiency will eventually reach a point of diminishing returns. At that point, AI can start moving the IT loads themselves: “Using the cost of compute history to do intelligent load balancing or container orchestration, you can bring down the energy cost of a particular application,” Ghosh told his DCD audience. This could potentially save half the IT energy cost, “just by reshuffling the jobs [with AI] - and this does not take into account turning idle servers off or anything crazy like that.”

Beyond that, Ghosh is working on AI analysis of the sounds in a data center.

“Experienced people can tell you something is wrong, because it sounds funny,” he said.

CIRC has been creating sound profiles of data centers, and relating them to power consumption.

Huawei is doing this too: “If there is a problem in a transformer, the pattern of noise changes,” said Zou Xiaoteng. “By learning the noise pattern of the transformer, we can use the acoustic technology to monitor the status of the transformer.”

This sort of approach allows AI to extend beyond expert human knowledge and pick up “things that human cognition can never understand,” said Ghosh.

“In the next 10 years, we will be able to predict failures before they happen,” said Ghosh. “One of my dreams is to create an algorithm that will completely eliminate the need for preventative maintenance.”

Huawei’s Xiaoteng reckons there are less-tangible benefits too: AI can improve resource utilization by around 20 percent, he told DCD, while reducing human error.

Xiaoteng sees AI climbing a ladder from level zero, the completely manual data center. “On level one the basic function is to visualize the contents of the data center with sensors,and on level two, we have some assistance, and partially unattended operation,” where the data center will report conditions to the engineer, who will respond appropriately.

At level three, the data center begins to offer its own root cause analysis and virtual help to solve problems, he said. Huawei has reached this stage, he said: “In the future, I believe we can use AI to predict if there's any problem and use the AI to self-recover the data center.”

At this stage, DCIM systems may even benefit from specialized AI processors, he predicted. Huawei is already experimenting with using its Ascend series AI processors to work in partnership with its DCIM on both cloud and edge sides.

Right now, most users are still at the early stages compared with these ideas, but some clearly share this optimism: “Today we use [AI] for monitoring set points,” said Eric Fussenegger, a mission critical facility site manager at Wells Fargo, speaking at DCD>New York in 2019, adding to DCIM and “enhancing the single pane of glass.”

AI could get physical, further in the future, said Fussenegger, in a fascinating aside. “The ink is not even dry yet, maybe it hasn't even hit the paper.” he said, but intelligent devices could play a role in the day-to-day physical maintenance and operation of a data center.

One day, robots could take over "cleaning or racking equipment for us, so I don’t have to worry about personnel being in hot and cold aisle areas. There are grocery stores that are using AI to sweep.”

Even these extreme views are tempered, however. Said Fussenegger: “I think we’re always going to need humans in there as a backup.”

DCD>Magazine The Artificial Intelligence Supplement

Out now

This article featured in a free digital supplement on artificial intelligence. Read today to learn about rack density, deep learning technologies, the role of CPUs in inferencing, the quest for fusion power, and much more. bit.ly/AISupplement

Issue 33 • July 2019 41

Cloud | Andy Lawrence

The cloud is not killing enterprise IT

Andy Lawrence Uptime Institute

We are still years from the tipping point when everything goes to the cloud, says Andy Lawrence

T

here are many things swelling the public cloud, but the industry is closely watching the rate at which enterprise workloads are moving there, because enterprise IT is a proven, high-margin sector with large, reliable budgets.

The popular view is that enterprise IT is rapidly migrating, as infrastructure layers are outsourced to cheap, reliable, flexible, pay-asyou-go cloud services - like AWS, Azure and Google Cloud. Many predict a tipping point where non-public cloud or non-software as a service (SaaS) IT becomes too expensive and too difficult to support.

Some studies suggest we are nearly there, but Uptime Intelligence believes the tipping point is some years away.

In the second half of 2018, 451 Research asked 1,000 operators where the majority of their workloads were deployed (Fig1). Sixty percent said most of their workloads were on-premises, some in private clouds. Only 20 percent said most of their workloads were in the public cloud or in SaaS. This took place when AWS had been available for 13 years, and Salesforce for 20.

The move could be stepping up: In 451 Research’s latest data, 39 percent of organizations say that by 2020, the bulk of their data will be in SaaS or a public cloud service - and nearly half (47 percent) say their workloads will still mostly be in enterprise data centers or under their control in a colocation facility. While shifting work to the cloud, many are simultaneously maintaining and even expanding premium data center and Edge facilities.

In 2019, Uptime Institute asked respondents to describe the percentages of their IT in different environments.

This study (Fig2) showed a much less dramatic shift. Over 600 operators said that, in 2021, about half of all workloads will still be in enterprise data centers, and only 18 percent of workloads in public cloud/ SaaS services. Just over three-quarters of all workloads, they believe, will still be managed

by their own enterprise staff at a variety of locations that include enterprise data centers, colocation venues, server closets and micro data centers.

So enterprise IT’s move to the cloud is real and strong, but it is still very premature to talk of the death of enterprise IT, and the corporate data center. It’s worth mentioning that a large part of the public cloud's growth is from consumer services, such as Netflix, Uber or Snapchat.

The tipping point may come eventually: workloads that have been converted to cloud are much more easily moved off-premises.

But we think the big switch is still a few years away: enterprise IT is changing and challenging, but not in terminal decline.

Public cloud operators will have to work on issues of transparency, trust, governance and service to attract IT workloads.

Outstanding tools and infrastructure are not enough.

More information is available to members of the Uptime Institute Network bit.ly/UptimeEnterprise

DCD>Awards Enterprise Data Center Design Award

Enter the Awards

To recognize the enterprise data center's vital role in business workloads, DCD's Enterprise Data Center Design Award is looking for projects with original ideas tailored for enterprise customers. bit.ly/DCDEnterpriseAward

42 DCD Magazine • datacenterdynamics.com

16th Annual

> San

Francisco

Global Content Partner

11-12 July 2019 // Marriott Marquis

Show Preview

See Inside Maximize your day: Experience the full conference program Registration data: Who is at the event Floorplan and exhibitors: Find your way around The best of: Speaker questions & answers 2019 Event Calendar

800+

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Follow us: @dcdevents #dcdsanfran Issue 33 • July 2019 43

San Francisco

> San Francisco Audience

> Insights from

registration data

Registration data gathered from more than 800 pre-qualified buyers of data center products and services in advance of the DCD>San Francisco conference is pointing to the fact that 2019 will be an important year for Edge, connectivity, thermal management and energy procurement.

43

16% Service Provider

54% Enterprise

As a conference producer, we are tasked with ensuring our conference agendas are engaging, purposeful and live on the ‘cutting edge.’ It can be challenging to decipher which content areas should serve as the pillars of the program and who should feature as the prominent thought-leaders. When approaching the curation of DCD>San Francisco, we wanted to ensure the program embodied the spirit of innovation, energyaware and sustainable values synonymous with the Bay Area.

Once in agreement on the program principals, we were able to scope out speakers deeply rooted and entwined with big tech in Northern California and kick start the agenda with jam-packed plenary roundtables centered on the infrastructure needed to power future industries and the route to cloud sustainability.

Together, we have crafted a compact program featuring over 50 top level speakers across unique plenary roundtables, panel debates, fireside chats and a host of engaging brunch and lunch workshops.

Kisandka Moses DCD Conference Producer

Overall buyer mix

30%

DCD>San Francisco regularly attracts a senior audience

51

AEC/ Advisory

17%

14%

Individual Specialist, Consultant Level

of Enterprise audience are Snr. Dir or VP level execs

C Level

26

Audience Seniority

26%

43%

Project, Technology or Team Leader

Senior Manager, VP and Department Head

of the local workforce are technology firms according to the Silicon Valley Leadership Group

> Speakers Adam Kramer Switch Anthony Despirito Schneider Electric Arman Shehabi Lawrence Berkeley National Laboratory Atle Haga Statkraft Bill Kleyman Switch Bob Woolley RagingWire Data Centers Bruce A. Taylor DCD Bruce Baxter CEI Modular Bruce Myatt Arup Chris DePrater Lawrence Livermore National Laboratory Chris Donnelly Switch Chris Orlando Scalematrix

Chris Pumphrey Douglas County Economic Development Authority Christian Laurenzano Janitza David Martinez Sandia National Laboratories Dean Nelson Uber Dr. Arman Shehabi Lawrence Berkeley National Laboratory Dr. Suvojit Ghosh McMaster University Eddie Schutter Switch Emily Sommer Etsy Eric Herzog IBM Gary Cook Greenpeace Gary Demasi Google George Rockett DCD

44 DCD Magazine • datacenterdynamics.com

Hans Royal Schneider Electric Ibbi Almufti SE Arup Isfandiyar Shaheen Facebook & NetEquity Networks Jake Ring GIGA Data Centers Jeff Omelchuck Infrastructure Masons Jeremy Rodriguez Nvidia Jim Collins Microsoft Jim Connaughton Nautilus Data Technologies Jim Richardson Avant Energy John Parker ESRI John Smith ABB Justin Jurek Piller Power Systems Inc Keith Dines Nvidia

> Top 10 most viewed

speaker profiles online With over 100 senior experts to hear from at DCD>San Francisco, below is a sneak-peak of our most popular and searched for speaker profiles online as we head into the 16th edition.

1

Dean Nelson Uber

2

Gary Demasi Google

3

Eric Herzog IBM

4

Hans Royal Schneider Electric

5

Bill Kleyman Switch

6

Steve Schwarzbek Northrop Grumman

7

Emily Sommer Etsy

8

Jim Collins Microsoft

9

Steve Press Kaiser Permanente

10

William Dougherty Omada Health

Kelly Morgan-Szydlo The 451 Group Kevin C. Kent The Ohio State University Wexner Medical Center Kisandka Moses DCD Mark Bulmer Georg Fischer Piping Systems Ltd Mark Hurley Schneider Electric Mark Thiele Ericsson Martin T. Olsen Vertiv Michael Oros Storage Networking Industry Association Mukesh Khattar EPRI Nicholas Paolo MTU Onsite Energy Oncu Er Avant Energy Patrik Öhlund Node Pole

Theme | Future Industries, AI and Connected Edge

Theme | Energy Smart Infrastructure

With an economy that outpaces the rest of the US, Northern California's Bay Area is the technology capital of the world. Home to half of the world's Internet giants and the launchpad for next-gen industries in healthcare, biotech and transportation - data centers and cloud infrastructure capacity are in huge demand. We are witnessing a keen desire from service providers to respond to the evolving infrastructure demands of cloud-native future industries - how will new infrastructure trends impact the design, location and ownership of the data center?

An energy-efficient approach to data center design and operations can reduce costs, improve uptime and drive new paths to sustainable infrastructure. From power and cooling innovations reducing costs inside the data center, to new business models, energy storage and PPA/ VPP relationships outside the data center, the sessions share best practices from the energy source to the data center itself.

Bill Kleyman of Switch takes aim at describing the data center of the future with a talk focused on meeting the digital needs of companies which were once analog. Eric Herzog, CMO at IBM Storage will deliver a presentation outlining the ability of cloud-based analytics and scalable, persistent cloud and physical storage to help companies unshackle their data and develop new business insights.

Producer's Highlight:

Plenary Roundtable: Dear Future Industries, Please Tell Us What Infrastructure You Will Need to Power Your Exponential Growth?

On July 11th at 2:30pm, infrastructure leads at digital native, compute-heavy companies will begin setting out their plans to keep up with previously unimaginable and surging scale’ with capacity planning, hardware ownership, IT agility and supply-chain management central to data center strategy. The panel will feature Skyler Holloway of Facebook, Northrop Grumman’s Steve Schwarzbek, Steven Press, VP of Data Center Operations at Kaiser Permanente, Eddie Schutter, CTO at Switch, LinkedIn’s Zaid Ali Khan and the Head of Uber Metal, Dean Nelson.

Peder Nærbø Bulk Infrastructure AS Peter Feldman Digital Crossroads Data Center Peter Jones Yondr Petter M. Tommeraas Basefarm Rebecca Scheel Innovation Norway Rich Scroggins Cummins Inc Roger Rogers IBM Russell Carr PE Arup Simon Allen Infrastructure Masons Skyler Holloway Facebook Stephen Worn DCD Steve Greenberg Lawrence Berkeley National Laboratory Steven Press Kaiser Permanente

Hans Royal, Director of Cleantech Client Development at Schneider Electric, will share expertise on the factors that are driving an industry-wide shift to clean power. This includes the rapidly falling costs of wind and solar, strategies to manage energy volatility with budget stability, and opportunities for owneroperators to differentiate their companies and exhibit leadership and innovation to their consumers and investors.

Looking inside the data center at critical infrastructure, Arup’s Bruce Myatt will lead a panel discussion consisting of thoughtleaders from Sandia National Laboratory, Lawrence Berkeley National Laboratory, Nautilus Data Technologies, Lawrence Livermore National Laboratory and Cupertino Electric, on the likely future of cooling in a data center dominated by AI and HPC workloads.

Producer's Highlight:

In Conversation with Microsoft Powering the Cloud, Optimizing Market Based Energy Rates and Bringing Value to the Data Center Industry The conference will conclude on July 12th at 3pm with a closing fireside chat featuring Jim Collins, Director of Energy at Microsoft and Susanna Kass, UN Sustainable Development Group.

They will discuss how the Internet giants providing the infrastructure to support adoption are tasked with balancing vast data volumes and compute, with sustainable expansion and energy procurement. This fireside chat will also focus on the benefits of optimizing for the future of energy procurement.

Steve Schwarzbek Northrop Grumman Corporation Sturgis Sobin Citigroup Energy Suresh Pichai Equinix Susanna Kass United Nations Sustainable Development Group Tony Despirito Schneider Electric Travis Wright QTS Data Centers Vincent Rais Uptime Institute William Dougherty Omada Health Yashar Barut Citigroup Energy Yigit Bulut EYP Mission Critical Facilities Zaid Ali Kahn LinkedIn

Issue 33 • July 2019 45

San Francisco

Day 1 | Thursday 11 July Conference Session Track Key

13:00

Expo Floor and Registration Opens

14:20

Opening Remarks

14:30

Hall 1 - Plenary Roundtable: Dear Future Industries, Please Tell Us What Infrastructure You Will Need to Power Your Exponential Growth? Dean Nelson, Uber, Steven Press, Kaiser Permanente, Zaid Ali Kahn, Linkedin, Skyler Holloway, Facebook, Steve Schwarzbek, Northtrop Grumman Corporation, Chris Orlando, Scalematrix, Eddie Schutter, Switch, [Panel Moderator] Kelly Morgan, 451 Group

15:40

Examining The Business of the Data Center Energy Smart Infrastructure Future Industries, AI & the Connected Edge

Afternoon Coffee, Expo, Speed Networking, and Innovation Stage

Hall 1

Hall 2

Hall 3

Hall 4

16:25

Why Cloud and Colocation Providers Are Switching to Renewable Energy Hans Royal, Schneider Electric

Meet the Demands of Your Data-Driven Infrastructure Eric Herzog, IBM Storage

Data Center 2020: Embracing the Digital Future with Switch Bill Kleyman, Switch

Boardroom Keynote: Insights into Geo-Expansion: Overcoming European Hyperscale Delivery Constraints Peter Jones, Yondr

16:55

No Limit Power: How To Support HPC at a PUE of 1.15 Jake Ring, GIGA Data Centers

Turning on 5G Martin Olsen, Vertiv

Webscale Resilience – Hazards Mitigation and Advanced Microgrids Ibbi Almufti SE & Russell Carr PE, Arup

Location, Location, Location: Which Regions Are Offering the Most Ping, Power and Pipe for Your Buck?

Chris Pumphrey, Douglas County Economic Development

17:25

Pricing the Bleeding Edge: How Will Coolants, Machine Learning and GPUs Impact The Cost Of The 2030 Data Center?

Dr. Suvojit Ghosh, McMaster University

Resiliency in hybrid IT: Notes from the field Vincent Rais, Uptime Institute

The Infrastructure Venture Democratizing Access to High-Capacity Fiber and Onboarding the Internetless Isfandiyar Shaheen, Facebook

Power(ful) Cities: How Location Is Dictating Energy Cost, Volatility and Contract Options Yashar Barut & Sturgis Sobin, Citigroup Energy

18:00

What does it take to take to be AI-ready? Building next generation data center environments Keith Dines and Jeremy Rodriguez, Nvidia

18:30

Drinks Reception and Networking on Expo Floor - Hosted by GF Piping Systems

20:00

Conference & Expo Ends

Lunches & Brunches | Day 1 VIP Lunch // 1:20-2:20pm | Nob Hill D Greening the Data Center - Opportunities in Norway Speakers: Rebecca Scheel, Invest in Norway, Atle Haga, Statkraft Host Norway Department, Peder Nærbø, Bulk Infrastructure AS, Petter M. Tømmeraas, Basefarm Hosted by: Innovation Norway

Innovation Stage // Expo Floor VIP Lunch // 1:20-2:20pm VIP & Press Room "Dear Future Industries" VIP Luncheon (by invitation only) Hosted by: DCD VIP

Day 1 3:45pm

Day 2 4:05pm

10:40am

Lunches & Brunches | Day 2 Brunch Briefing 10:30-11:30am | Nob Hill C

Brunch Briefing 10:30-11:30am | Nob Hill D

Lunch Briefing 1:20-2:30pm | Nob Hill C

Lunch Briefing 1:20-2:30pm | Nob Hill D

Leveraging the Benefits of Manufactured Data Center Deployments Hosted by: Bruce Baxter, CEI Modular

How to Build Earthquake Resistant Data Centers Hosted by: Bob Woolley, RagingWire Data Centers

Next Generation Digital Data Center Operations Hosted by: Tony DeSpirito, Schneider Electric

Connectivity: Finding the Right Balance Between Price and Performance in your Next Colo Contract Hosted by: Chris Donnelly, Switch

46 DCD Magazine • datacenterdynamics.com

Day 2 | Friday 12 July 8:00

Expo Floor and Registration Opens

9:20

Opening Remarks

9:30

Hall 1 - Plenary Roundtable: Energy Smart Special - What Breaks When the Whole World is Online? Gary Demasi, Google Adam Kramer, Switch, Emily Sommer, Etsy, Dr. Arman Shehabi, Lawrence Berkeley National Laboratory, Gary Cook, Greenpeace, Susanna Kass, United Nations Sustainable Development Group, Travis Wright, QTS, [Panel Moderator] George Rockett, DCD

10:30

Coffee Break, Speed “Energy Smart” Networking and Innovation Stage

Hall 1

Hall 2

Hall 3

Hall 4

11:20

Case Study: How Onsite Renewable Generation and Lake Water Cooling Gave Rise to the Green Zettabytes John Smith, ABB Peter Feldman, Digital Crossroads

Consumers Care About Carbon: Could a ‘Green Data’ Label Prompt You To Keep Your Emissions in Check?

Patrik Öhlund, Node Pole

Can HDDs Keep Up with the Analytics Arms Race?

Michael Oros, Storage Networking Industry Association

Fireside Chat: Corporate PPAs and Beyond: How to Achieve Zero Carbon Energy for the Data Center Gary Demasi, Google Susanna Kass, UN Sustainable Development Group

11:50

From Core to Edge: How to Implement Smart Power in the Data Center Justin Jurek, Piller Power Systems

Going Green with Data Center Builds, Consolidations and Retrofit John Parker, ESRI

The Battle Between Edge and Core for Supremacy: Which Deployment Will Win? Mark Thiele, Ericsson

A New Approach to Electric Supply for Data Centers Oncu Er and Jim Richardson, Avant Energy

12:20

Hosted Roundtables: 12:20pm - 1:20pm

Panel Debate: Preparing For Decarbonization - Can Microgrids ‘Cross the Chasm’?

Yigit Bulut, EYP Mission Critical Facilities Mukesh Khattar, EPRI Suresh Pichai, Equinix Kevin Kent, The Ohio State University Wexner Medical Center

Panel Debate: Prepping for Dense Data: How Long Will Air Be Able to Handle the Heat From Cognitive Workloads?

Chris Deprater, Lawrence Livermore National Laboratory David Martinez, Sandia National Laboratories James Connaughton, Nautilus Data Technologies Bruce Baxter, CEI Modular Steve Greenberg, Lawrence Berkeley National Laboratory

Infrastructure Masons Leadership Summit 2019 Jeff Omelchuck & Simon Allen, Infrastructure Masons

Head to speaker meeting point to reserve a seat.

Panel Moderator: Bruce A. Taylor, DCD

Panel Moderator: Bruce Myatt, Arup 13:20

Lunch, Innovation Stage and VIP Lunch(es)

14:30

Plenary Fireside Chat: In Conversation with Microsoft - Powering the Cloud, Optimizing Market Based Energy Rates and Bringing Value to the Data Center Industry // Jim Collins, Microsoft | Susanna Kass, United Nations Sustainable Development Group

15:00

Conference and Expo Ends

17:00

DCD VIP Networking Party (Invite Only)

Hosted Roundtable // 12:20pm - 1:20pm | Main Plenary Hall 1 1. Ready To Go Off-Grid? Are Microgrids in Your Future?

Nicholas Paolo, MTU Onsite Energy

2. Boom or Bust: How Will “New” IT Trends Impact the Business Model of the Data Center?

William Dougherty, Omada Health

DCD - Global Discussions

3. What is Hiding in Your Data Center? The Impact of Indirect Costs in x86 and LinuxONE Environments Roger Rogers, IBM

4. Utilizing Software Solutions for Data Center Optimization & TCO Mark Hurley, Schneider Electric

Datacentre Dynamics

5. VIP Only Roundtable: Off The Record - Strategies for ‘Greening’ the Data Center An Array of Energy Experts

@DCDEvents #DCDSanFran

Issue 33 • July 2019 47

San Francisco

> Sponsors, Exhibitors & Partners > Knowledge Partners

With data center growth continuing, business models are evolving and stakeholders are converging. Global site selection remains a key strategic challenge for colos, determining prospective tenants and revenues, total cost of ownership and energy cost. Evaluation factors are multi-faceted and complex, with environmental risk, taxes and regulations, transportation, construction and speed to market all playing a pivotal role. With huge 24/7 power demands, however, energy availability and grid mix are also key.

MODULAR

Hall 4

DCD Office Innovation Stage Sponsored by

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Brunch/ Lunch Briefings

Nob Hill C

> Exhibitors BTECH Cupertino Electric, Inc. Chatsworth Products Cummins Inc DCPro Digitalor Technology Co,. Ltd East Penn E+I Engineering EnerSys Enlogic: a division of CIS EYP Mission Critical Facilities Forced Physics DCT Golden Bay Fence Plus Iron Works, Inc. Harbor Enterprises IBM Janitza Kohler Power Systems Motivair MTU Onsite Energy

On July 11, we will explore global site selection with Douglas County Economic Development’s Chris Pumphrey uncovering the regions that offer the most ping, power and pipe for your buck. Gary Demasi, Google, will sit opposite Susanna Kass, UN Sustainable Development Group, for a fireside chat rooted in energy strategy beyond corporate power purchasing agreements, as Google takes aim at zero carbon emissions for the data center.

Catering Station

Private Meeting Room 1

VIP & Press Room

13

Registration

Private Meeting Room 2

Main Plenary Hall 1

Catering Station

Private Meeting Room 3

12

Theme | The Business of Data Centers

Piller Power Systems Power Distribution, Inc Powersmiths International Corp RagingWire Data Centers Raritan Schneider Electric Starline Submer Immersion Cooling Subzero Engineering Sunbird Software Survive-a-Storm Shelters TATE Access Floors Inc Thomson Power Systems Toshiba TrendPoint United Rentals Uptime Institute Vertiv Victaulic

48 DCD Magazine • datacenterdynamics.com

Hall 2

Producer's Highlight:

Power(ful) Cities: How Location Is Dictating Energy Cost, Volatility and Contract Options Yashar Barat and Sturgis Sobin of Citigroup’s Energy and Commodities Division will join forces to highlight the importance of site selection and location to attractive renewable deal-making. Join this session to demystify the complex economics of power in both regulated and deregulated markets, learn how owner-operators are achieving their renewables aims, managing their exposure to commodity prices, and increasing budget certainty.

Uncover new technologies from 38 exhibits on the show floor

45%

of the audience are exploring backup generation, UPS, water and air cooling solutions

The Best Of: Speaker Q&As Dr. Suvojit Ghosh, McMaster University

Q:What is your biggest headache as it pertains to setting up your data center?

Q:You've spoken quite a bit on liquid cooling, where you think that trend is going and the fact that it's pretty much inevitable in terms of the densities that are coming of age in data centers today.

Do you draw any sort of distinction between where you think the onboarding of direct-to-chip cooling will come of age versus immersion cooling?

A: A lot of my headaches [as it pertains to] setting up data centers is [related to] knowing what to ask companies like Schneider Electric ‘what's available and what will really work?’ The conversation around where to source GPUs and FPGAs is fun. But understanding the infrastructure details like in-rack cooling, whether we want to do the networking in-rack or above the rack, or whether we want to set-up a backplane for the networking... how will we hit our green targets, our performance targets and our implementation targets that matters. With a lot of those details, if you do not get them right, your choice of chip and networking in technology at the node isn't going to matter. If the power isn't getting to the chip and you have insufficient cooling, all of your hard work at the chip level will have been in vain. One of the things I've learned, kind of painfully, is you really need to walk the floor - raised floors, lower floors - learn about the cooling system, know how many tons of cooling you're going to need and do all of those calculations. Because if you don't do the facilities work, the rest of it's just not going to come together.

A: I agree that enterprises have a lot more confidence in direct-to-chip, mainly because it's been around for 30 or 40 years now. IBM has been using direct-to-chip cooling for a long time, especially in their vector CPU and supercomputing platforms. Immersion cooling is newer [than direct-to-chip] but it's not a new technology because enterprises have been using immersion for power electronics and transformers for a while.

I don't think there would be one standard cooling method, the route chosen will be very application specific. What we would probably end up seeing is the hyperscalers and large wholesale colocation providers moving towards immersion as it's easier to maintain when it's centralized. Whereas, when you're moving closer and closer towards the edge, and you have a single rack or a half rack, immersion will be very difficult to maintain because it requires a lot more maintenance than direct-to-chip. In unmanned locations or small 'Edge' sites, you're more likely to see use of directto-chip.

Steve Schwarzbek, Northrop Grumman Corporation

Bill Dougherty, Omada Health Q:What factors do you think are driving site selection at present?

A: In my opinion, decisions on site selection will be driven by cheap water, cheap power, low natural disaster risk and low taxation. I don't see these decisions being driven by the need to be closer to end-users - it'll be economics, with cost becoming more important than latency when trying to figure out what the next data center market will be.

As CTO of RagingWire, we loved being in Sacramento. Before I joined the company, I was a repeat customer because they (RagingWire) had the lowest natural disaster risk in the United States, the cheapest power and it was a low tax zone. As an end-user, my colocation cost in Sacramento was lower than my colocation cost in San Jose. The decision had nothing to do with latency to my end-users.

Check out our new website for the most up to date event details datacenterdynamics.com Issue 33 • July 2019 49

New York

>2019 Event Calendar Dallas

London DCD> Sydney 15 August DCD> Santiago 10 September DCD> Singapore 17-18 September DCD> México 25 September

22 October 2019

5-6 November 2019

Fairmont Dallas

Old Billingsgate

The Dallas-Fort Worth area is an economic powerhouse supporting multiple tech enabled sectors such as defense, financial services, information technology and transportation. Dallas itself serves as one of the most important telecoms interconnection points in the country making it a magnet for data center activity.

London has remained the largest of the European data center markets with an estimated 488MW of data center capacity, despite recent economic and political pressures.

This national event pulls together the most senior decision makers from the world of colocation, cloud and telco data centers to discuss how the next generation of Infrastructure-as-a-Service will be designed, built and interconnected.

Attendees Include:

Billed as a pan-European event, DCD>London attracts senior delegates from all of the continent's colos and hyperscalers, most of whom have a significant presence here, whilst its Enterprise audience is mostly drawn from the UK itself.

DCD> Dallas 22 October DCD> London 5-6 November DCD> São Paulo 5-6 November DCD> Mumbai 20 November DCD> Beijing 5 December

Attendees Include:

/datacenterdynamics

®

/datacentredynamics

@DCDEvents

Check out our new website for the most up to date event details

datacenterdynamics.com 50 DCD Magazine • datacenterdynamics.com

Design + Build | Think different

Not just stacks of Macs in racks

Sebastian Moss Deputy Editor

MacStadium runs Apple hardware in its data centers. That’s a surprisingly sensible thing to do, Sebastian Moss discovers

T

here's no reason to use Apple hardware at the bottom of your private cloud, unless you have to.”

Shawn Lankton, chief revenue officer of MacStadium, is honest about why his company exists.

It’s not there to compete with standard colocation companies on cost, latency or geographic distribution. Instead, he hopes to target a very specific niche: “We exist because Apple requires their customers to work with Mac infrastructure at the bottom of their stack. And that's not our call, that's just a problem that we endeavor to solve.

“We have come up with technology, infrastructure, expertise, best practices to make that possible, easily and at scale.”

Targeting companies that need to use the Mac OS, such as Xcode app developers, MacStadium operates server racks full of tens of thousands of Mac computers across five data center locations - Atlanta, Las Vegas, Silicon Valley, Frankfurt and Dublin.

Primarily, MacStadium uses private suites in Equinix and Zayo data centers, but has a small footprint in a Keppel facility in Dublin.

"We have plans of getting to Asia in the near future and launching additional

points of presence," Lankton said. "But we don't really need to be within 50 miles of a customer for them to have a good experience, because the use case is not super latency dependent.

“If you're sending a build or a test job to one of our data centers, that may be a three minute or a three hour job. And so if it takes an extra few milliseconds to travel to the data center, that doesn't really impact the performance or the user experience."

As a business, the company started “with a bare metal offering of Mac minis and Mac Pros,” Kevin Beebe, MacStadium’s VP of product management & security, told DCD.

Issue 33 • July 2019 51

“And as our product evolved, we got into virtualization and led the initiative to get Mac OS and Apple infrastructure supported within VMware.

“In the last two years, our revenue has exponentially grown in the enterprise market - 70 to 80 percent of our revenue is from that customer base now.”

Core to wooing that enterprise audience is being able to offer the reliability, compliance and security standards of conventional data center equipment. Forced to rely primarily on consumer equipment designed to live under a desk, this can prove challenging.

To deploy the various flavors of Mac systems, MacStadium designs its own racks. Originally, “we had tons of Mac minis deployed in fully custom racks that are 10 feet tall and double wide and very non-standard,” Lankton said. "We still have lots of customers deployed in those racks, but for the 2018 Mac minis and for all the Mac Pros and for anything that comes after, we have designed racks that fit in standard 19” designs. That’s to allow us to deploy in more points of presence, and to deploy more rapidly.”

Currently, the company's most popular device, the one it still deploys in the largest volume today, is the 2013 Mac Pro. Unfortunately, the cylindrical design referred to disparagingly by some as a 'trash can' - does not immediately lend itself to being stacked in a rack.

"How do you get a round peg in a square hole? We have a custom designed and manufactured sled,” Lankton said. "We add the enterprise upgrades that allow it to go from a consumer device that has a 1GbE port and a single power supply to something that looks like an enterprise server when presented to a hypervisor layer like VMware

that has dual Ethernet, dual SAN fabric connection, and dual power.“ When Apple updated the system in 2018, it kept the same shape and size. “People would look at that and say, 'Well, it wasn't a big design change, you shouldn't have to do a whole lot of work,'” Beebe said.

“But the power management and thermals are significantly different, and they updated from 1GbE to 10GbE network interfaces. So we did a whole Cisco Nexus spine leaf architecture for the new racks that we were designing. It ended up being a lot bigger initiative than most people thought it would be.”

Lankton added: “The ports are in a different place, the airflow intakes and outputs are in a different place. The cooling and power requirements are massively different. We had to start from scratch and deploy brand new racks for those in about two months.”

The company faced a similar dilemma with the iMac Pro, an all-in-one machine where the computer is built into the screen.

“It's a tragic waste of a 5K screen: nobody's looking at them in the data center,” Lankton said. The company fits 24 of the machines in a standard 19in rack, with two systems packed together, screens facing but disabled.

“It's one of the realities of dealing with Apple hardware,” Lankton said. “We get what we get. And we won't build custom hardware on that front: Apple trusts us to take their hardware and be true to what they have produced.”

Therein lies the greatest potential weakness in MacStadium’s business model: They serve at Apple’s pleasure. "We talked about actually taking the screen apart and trying to just rack mount the internal components," Beebe said of the iMac. "And, that would be frowned upon by Apple and non-supportable going forward."

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52 DCD Magazine • datacenterdynamics.com

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Design + Build | Think different Perhaps it’s an imbalanced relationship, where power rests with Apple, but MacStadium is confident that it has nothing to fear. Apple is out of the enterprise infrastructure business, and has no need to compete, while the existence of MacStadium makes it easier for businesses to develop apps and test them with Safari.

In recognition of this, Apple highlighted MacStadium during its October 2018 event.

"There's always been the question of how much Apple would support or acknowledge us and so that kind of validated that," Beebe said. "I think even a lot of customers have had hesitancy of using MacStadium or any Mac hosting company. Apple has a very strict enterprise licensing agreement that's kind of open to interpretation but, being on stage, validated that they know exactly what we're doing and find that to be acceptable."

For many in MacStadium, the on-stage shout-out was a surprise. “As usual with Apple, we didn't know anything beforehand,” Lankton said. “There were only one or two folks inside the organization that had advanced knowledge, even as a camera crew was coming through our data center to photograph the Mac minis.”

The same level of secrecy shrouded Apple’s latest event, held this June. "We had no advanced tips from the Apple team," Lankton told DCD, days after the conference.

There, Apple announced a new Mac Pro, the first major redesign in years.

Packed with Xeon CPUs that go up to 28 cores and 1.5 terabytes of memory, as well as AMD Radeon Pro Vega II GPUs, the new Pro is an expensive rectangular beast.

"There have been estimates that the fully spec'd version might cost upwards of $35,000," Lankton said. "It's obviously a super impressive machine."

It will also come in a 4U rack mountable version, built by Apple. So where does that leave MacStadium? "Anybody can put a Mac Pro in a data center," Lankton said.

"Where we stand out is knowing how to configure those Mac Pros, having a supply chain to deploy them, having Mac expertise on staff, and knowing how to deploy those Macs in a cloud which allows customers to really treat that infrastructure as code rather than just have a Mac in a data center."

The company won't know whether it will have to make changes to the rack mountable system until it gets its hands on the Pro - later this year, at the same time as everyone else. "If there are expansion modules to provide enough network connectivity and if we can work with the power modules to connect that to our redundant power systems, then we may not have to do anything proprietary to it," Lankton said.

"But there's a big difference between rack mountable and enterprise-ready server.

"Anything that's rectangular you can put in a rack, but you need to have full redundancy and supportability to really call it an enterprise machine. And so we'll make whatever upgrades we need to make."

While the company has received some interest in deploying the system, it also suffers from the same issue impacting all 2018 Mac systems - the T2 Security Chip.

An Apple custom silicon chip, T2 stops Macs from running any operating system

that's not signed by Apple - including Linux or VMware ESXi. "None of those devices are VMware Certified," Beebe said. "We're trying to bring Apple to the table to help solve that, because Apple and VMware haven't [worked] so well directly together in the past. We've had to play middleman in getting that done."

This is why the 2013 Mac Pro remains the company's most popular device, as it shifts further from 'Macs in racks' to providing more cloud services.

"We've done that since the very beginning using VMware as a hypervisor platform," Lankton said, "but more recently, we've supported a new Mac-only hypervisor called Anka, and then Orka is the next iteration of that."

Orka is the company's own in-house virtualization layer for Mac infrastructure based on Docker and Kubernetes technology.

"That's really purpose built for the users that need to have infrastructure that looks and feels as generic as possible, while still fitting inside of Apple's requirements that all app builds be done on Xcode," Lankton said.

"What we want to do is help our customers abstract that infrastructure layer as much as possible, so that their iOS and Mac app builds and tests look more and more like the same thing for Android or back-end server code or anything else."

He added: “Our customers don't just want a Mac in a rack, they want a Mac in a cloud.”

"As usual with Apple, we didn't know anything beforehand"

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Issue 33 • July 2019 53

Driven.

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Don’t just take our word for it.

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Servers + Storage | Max's memory

Servers need more memory

Max Smolaks Contributor

Consumer devices have all the memory they need, but AI applications in data centers still want more. Max Smolaks hears how Kingston is responding

K

ingston Technology has been a part of the memory landscape for more than 30 years. It makes everything from SD cards for consumer devices to server SSDs (solid state drives), and more recently has started selling literally flashy gear for gamers. Its first ever product, and its core competency, is DRAM - it is the world’s largest privately held producer of volatile memory.

Kingston was co-founded in 1987 by its current president John Tu, and COO David Sun, in response to a chip shortage. Today, it has manufacturing and logistics operations in the US, the UK, Ireland, Taiwan and China.

A team from Kingston recently visited DCD to chat about RAM - here’s a public service announcement on why you shouldn’t overlook this humble server component.

“People think that RAM will just install itself,” said Adrien Viaud, senior technology manager at Kingston. “Yes, it’s probably a small aspect of your infrastructure, but it’s a major cost on the server side.

“You would be surprised how uninformed some of our customers sometimes are, saying 'we put in whatever.' Then they pay extra for that faster memory which clocks down due to a particular processor.

“It’s all about education: for example, DDR4 LRDIMM has extra data buffers on the PCB, that will generate better performance, of course, but more heat and more power. It’s kind of funny, because when we go to the Middle East, we ask if they are worried about power consumption and they don’t care. They have money to pay for cooling, even if it’s in the desert. Now,

when we go to the UK, they say they have to be careful because the National Grid is really constrained. You have to bear all of this in mind.”

“We want to be a trusted adviser to our partners,” chimed in Miriam Brown, B2B strategic marketing manager for EMEA. “If they want to talk about scalability, power, capacity, performance - the guys will advise based purely on business needs. They understand applications that are working in the data center.”

According to Viaud, estimating RAM power consumption is a tricky business: it depends on the load, type of module, chip capacity and density, and proprietary lithography - a piece of silicon made by SK Hynix can be very different than one made

by Micron.

"People pay extra for faster memory that clocks down: it's all about education"

One of the main benefits of having lots of RAM is it helps consolidate expansive server farms into more manageable proportions.

More powerful - and thus often more efficient - servers will increase rack density, which can help save money on expensive square feet in colocation facilities.

“By upgrading your memory, you could potentially consolidate your four current servers into one. You save on rack space, you save on cooling, you save on everything.

The question is - do we upgrade or do we go for the next generation of server?” Viaud said.

Issue 33 • July 2019 55

Servers + Storage | Max's memory In the past, a lot of the growth in the memory market was driven by the consumer sector - since operating systems were massively hungry for RAM but capacities weren’t high, and upgrades were frequent.

But their memory demands seem to have stabilized around 8GB - so now memory vendors are all about data centers.

“That’s where capacity growth is going,” said Pasi Siukonen, Technical Resources Group team leader at Kingston. “Typical desktop or laptop users, when they are following their OS recommendations, we have seen from Windows 7 to 8 to 10, that there’s not a lot of memory requirement on the OS side, it’s the same on MacOS and Linux.

“So we see a lot more of the memory investment on the server side.”

By the way, those fancy AI applications

that everybody is talking about? They love to munch on memory. Sanjay Mehrota, CEO of massive memory-maker Micron, recently told analysts that AI servers need six times more DRAM than servers for traditional workloads.

“On the RAM side, we can see 128GB space per module, and soon it will be 268GB per module. Imagine the capacities we can reach,” Viaud said. “DDR5 starts at 3.2GHz; latencies are going to go up as frequency goes up, voltage goes down to 1.1V - DDR4 was 1.35V overclock or 1.2V standard voltage.”

Absolutely fabless The interesting fact about Kingston is that even though the company made $6.7bn in 2017, the last year for which there’s available data - it does not manufacture its own memory chips.

56 DCD Magazine • datacenterdynamics.com

Instead, it focuses on customization and assembly across eight manufacturing plants with 60 surface mount technology (SMT) lines, rumored to be churning out more than twenty-five million memory modules every month.

In a world where silicon has become a commodity, being fabless makes sense: Nvidia, Qualcomm, Xilinx and AMD, which all went fabless, can all attest to that, having moved to fabless operations years ago.

“You have to remember, if you look at the DRAM landscape in the nineties, there were like 20 DRAM vendors. There was Toshiba, Qimonda, Elpida. There’s four left now because it’s such a dangerous market to go in. And this is why they probably made a wise decision to not get involved in manufacturing of wafers.” Viaud said.

www.fossilfreedata.com

Crossword | Get cross with words

Data center crossword #1 We like a puzzle at DCD - so to help tide you over until the next issue of the magazine, here is a dastardly crossword devised by Peter Judge and Zach Lipman

Across 1. E uropean found after Nordic node perhaps? (4) 3. Court type of processor, half missing (4) 6. Thank you after US chief prosecutor’s brief information (4) 10. Strangely glad urine limits semiconductor chemicals (7,8) 11. Small fish defence method to avoid volt jolt? (5,10) 13. Diesel emergency recovery system labels, something data center technicians might wear to DCD events?

(9,6) 15. Take the edges off lath, long, with butcher’s brand of chip? (6,9) 20. & 26. It's bedtime for waiter with accommodation, space for unattended racks?

(6,3,6,4) 23. Investment goes into this for Equinix and other public companies (4,6,5) 25. In vest,alternative way to get into the US (4) 26 (see 20) 27. Equipment already loved (4)

Down 1. Aeons after potential leaves?

(5) 2. Lithium 50 before delicate bell chime, melodic and soulful (8) 4. German town with reduced ultra-low maintenance batteries (3) 5. Dim cots confused short terms of service for abbreviated data center management tool? (4,3) 6. Dory, lacking tail, in charge for column (5) 7. Titanium on its last legs: what most cables need! (7) 8. Basic computing unit, German one before Toronto

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– this is what we’ll do to Apple (4,4) 9. Dodgy NASA ruse consumes networked storage (1,3,4) 12. Short vulnerability executes code remotely (3) 13. Timeless ten, adept with Religious Studies, are people who make things possible (8) 14. A rea too small for a hyperscaler (8) 16. Male butters* (2-5) 17. C onspirators’ output devices (7)

58 DCD Magazine • datacenterdynamics.com

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18. H ow DEC programmers debugged their output (3) 19. Tastes South African potential that we own(7) 21. A subject, pronoun and indefinite article (5) 22. Given an Uptime Tier? (5) 24. A lifetime of cost (3)

27

Answers will be published in the next issue of DCD magazine * Contact DCD if you can find any connection between this word and data centers

If you manage to complete it, let us know at editorial@datacenterdynamics.com

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