1 IDL - International Digital Library Of Technology & Research Volume 1, Issue 6, June 2017
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Fog Computing – Enhancing the Maximum Energy Consumption of Data Servers. Priyanka Chettiyar1, Prabadevi B2and Jeyanthi N3 School of Information Technology, Vellore Institute of Technology, Vellore, Tamil Nadu. priyanka.mannarsamy@vit.ac.in prabadevi.b@vit.ac.in njeyanthi@vit.ac.in
Abstract Fog Computing and IoT systems make use of end-user premises devices as local servers. Here, we are identifying the scenarios for which running applications from NDCs are more energy-efficient than running the same applications from MDC. With the complete survey and analysis of various energy consumption factors such as different flow-variants and time-variants with respect to the Network Equipment we found two energy consumption use cases and respective results. Parameters such as current Load, Pmax, Cmax, Incremental Energy etc evolved with respect to system structure and various data related parameters leading to the conclusion that the NDC utilizes relatively reduced factor of energy comparative to the MDC. The study reveals that NDC as a part of Fog makeweights the MDCs to accompany respective applications, especially in the scenarios where IoT based applications are used where end users are the source data providers and can maximize the server utilization.
Index Terms— Centralized Data Servers, Cmax, Energy expenditure, Fog Computing, Nano Data Servers, Pmax.
1.
INTRODUCTION
loud computing and respective cloud relative
Capplications are on increasing demand and growing
demand
and
growth
of
various
smart
devices
communicating and making the world more connected,
swiftly in this digital sector of technology. Studies
well known as IoT. Recent surveys have expressed the
until date reflects cloud computing as the highly energy
fact that soon nobody can stop IoT from transforming
efficient for processing any job instead of running it
the traditional technology to digital world rapidly. Cloud
locally. Nevertheless, when energy utilization evaluated
computing appeared where application services easily
with respect to network topology and some other factors
made available to end users as frameworks, platforms
such as power consumption due to interactive cloud
and softwares. Cloud computing, still cannot be termed
services at the end user side; energy consumption
as “A platform for all” as it lags various issues to meet
seemed to be varying with respect to various use cases.
the requirements of IoT applications.
The pervasiveness of universality for the increasing IDL - International Digital Library
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Fog computing also known Fog networking or
any network architecture, various energy consumption
fogging, or Edge computing is a concept in which clients
models are put forward based on their content
or intermediate users near to end users can accumulate
distribution. Two types of network equipment are
ample amount of capacity in order to perform the same
studied as Shared network and Unshared network.
communication and provide similar services in way that
Shared network equipment is when many users share
is more efficient rather than controlled over the central
equipment and services. Unshared network equipment is
cloud servers. It can recognize any capacious cloud
a network where the equipment or services situated at
server or any big data structures, where accessing data
end users is shared by single user or to a limited fixed set
can be a troublesome task. To make computing possible
of users. Initially, a complete end-to-end network
in an end-to-end manner for any network topology
architecture is used in which all-necessary data required
where new services and required applications delivered
for processing from NDC and Central Data center is
more efficiently and easily to millions of smartly
present. As we are aware that the data or information is
interconnected devices, fog was introduced. The
processed and located in the data servers of the cloud
interconnected fog devices are mostly consisting of set-
storage, the attire need to understand the energy usage of
top-boxes, access points, roadside units, cellular base
data servers is focused. Since the data in Cloud services
stations, etc. A 3-level hierarchy formed in the process
is processed and stored in data centers, an obvious focus
of a complete end-to-end services delivery from cloud to
for studying energy consumption of Cloud services is the
smart devices.Thus, fog computing is nothing but an
data centers. Nonetheless, even the transport network
Intermediate node between the end user smart devices
which routes the end users to the cloud servers play a
and centralized cloud data centers extending the
visible role in energy utilization. Normally, when the
functionality of cloud computing in way that is more
end users access the cloud servers, a subtle amount of
flexible. Fog computing turning out to be more popular
energy
for enormous number of applications with respect to
improvement in energy consumption in the transport
IoT. Here, we often use a term as “Nano Data Servers�
network and end user smart devices will help improving
(NDC) which are nothing but small storage capacity
the performance of end NDC. The experimental results
servers, which are present in end user locations used for
show that the Nano data servers can obverse Centralized
inter-communication of data with its peers.We can state
Data Servers and reduce the energy consumption
that Fog Computing is a paradigm that brings cloud
for the appliances that can be easily migrated from cloud
computing at the edges of the network topology.
servers to NDC. The following figure explains broadly
In this work, we try to find out the different use cases in which when the application is running on NDC is more efficient than the centralized cloud server is.For
is
consumed.The
the fog node and its role.
statistics
reveal
that
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the various implementations of fog respective to its heads and tails. A] Fog – IoT Platform. IoT brings more than a hazardous conception of endpoints, which is problematic in a few ways.[1] In this part, analysis of those interruptions is done, and a progressive appropriated engineering that stretches out from the edge of the system to the center nicknamed Fog Fig 1. Fog Computing.
Computing is proposed. Specifically, focused on another dimension precomputing to Big Data and Analytics by
Many more interesting features which fog computing made available to us are Knowledge of locationtracking
end
user
devices
helping
motion,
IoT: an enormously distributed no. of sources at the ends.
the
hierarchical interplays between fog, cloud and the end
B] Internet – Nano Data Centers.
user devices signifying how fog node gets local
The growing concern about Energy utilization in the
overview when global overview was possible only at
modern data centers, gave rise to the model of Nano
higher
modifiable
Data Centers(NaDa). [7] ISP-controlled home gateways
optimizations depending on client side network and
were used to facilitate computing services and storage as
applications, improved caching methodology, end user
well. It also forms a distributed architecture with peer-to-
smart devices knowledge etc
peer data center model. Video-on-Demand (VoD)
The key to handle and manage the analytics rapidly with
services used to verify the actual capability of NaDa.
the help of data provided by IoT applications made
We develop an energy consumption model for VoD in
possible by fog data processing.
traditional and in NaDa data centers and evaluate this
level,
real
time
computation,
model using a large set of empirical VoD access data. We find that even under the most pessimistic scenarios,
2.
RELATED SURVEY.
Fog computing and its services are rapidly growing in every other sector with a purpose adding to our global digital revenue. Let us have peek overview of
NaDa saves at least 20% to 30% of the energy compared to traditional data centers. These savings stem from energy-preserving properties inherent to NaDa such as the reuse of already committed baseline power on underutilized gateways, the avoidance of cooling costs,
4 IDL - International Digital Library Of Technology & Research Volume 1, Issue 6, June 2017
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and the reduction of network energy consumption because of demand and service co-localization in NaDa.
[D] Fog Computing Saving Energy. In this paper, a comparison of energy utilization of
C] Green Cloud computing: Balanced Energy
applications on both the servers is done and the results
Management.
shows that Nano data servers can save energy
Network-based cloud computing is rapidly expanding as
comparatively on a higher rate based on various system
an
designing factors[9]. The hopping rate also adds to little
alternative
to
conventional
office-based
computing[8]. As cloud computing becomes more
extent with the various factors.
widespread, the energy consumption of the network and
Also, found that some part of energy which is consumed
computing resources that underpin the cloud will grow.
currently can be saved by bringing few applications at
This is happening at a time when there is increasing
the Nano platform level.
attention being paid to the need to manage energy consumption
across
the
entire
information
and
E] Document Processing – Energy Consumption
communications technology (ICT) sector. While data
Cloud computing and cloud-based services are a rapidly
center energy use has received much attention recently,
growing sector of the expanding digital economy.
there has been less attention paid to the energy
Recent studies have suggested that processing a task in
consumption of the transmission and switching networks
the cloud is more energy-efficient than processing the
that are key to connecting users to the cloud. In this
same task locally [10].However, these studies have
paper, we present an analysis of energy consumption in
generally ignored the network transport energy and the
cloud computing. The analysis considers both public and
additional power consumed by end-user devices when
private clouds, and includes energy consumption in
accessing the cloud. In this paper, we develop a simple
switching and transmission as well as data processing
model to estimate the incremental power consumption
and data storage. We show that energy consumption in
involved in using interactive cloud services. We then
transport and switching can be a significant percentage
apply our model to a representative cloud-based word
of total energy consumption in cloud computing. Cloud
processing
computing can enable more energy-efficient use of
measurements that the volume of traffic generated by a
computing power, especially when the computing tasks
session of the application typically exceeds the amount
are of low intensity or infrequent. However, under some
of data keyed in by the user by more than a factor of
circumstances cloud computing can consume more
1000. This has important implications on the overall
energy than conventional computing where each user
power consumption of the service. We provide insights
performs all computing on their own personal computer
into the reasons behind the observed traffic levels.
(PC).
Finally, we compare our estimates of the power
application
and
observe
from
our
5 IDL - International Digital Library Of Technology & Research Volume 1, Issue 6, June 2017
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consumption with performing the same task on a low-
utilizing mist figuring. For an internet amusement utilize
power consuming computer. Our study reveals that it is
case, we found that the normal reaction time for a client
not always energy-wise to use the cloud. Performing
is enhanced by 20% when utilizing the edge of the
certain tasks locally can be more energy-efficient than
system in contrast with utilizing a cloud-just model. It
using the cloud.
was additionally watched that the volume of movement between the edge and the cloud server is lessened by
F] Architecture - IPTV networks
more than 90% for the utilization case. The preparatory
Another Energy utilization model of IPTV stockpiling
outcomes highlight the capability of haze processing in
and dissemination gives bits of knowledge into the ideal
accomplishing a
outline of a VoD organize.[11] Energy utilization is
highlights the advantages of incorporating the edge of
limited by repeating mainstream program material on
the system into the figuring biological community.
practical registering model and
servers near clients.
3.
G] Fog – Potential The
Internet
of
Things
(IoT)
could
PROPOSED WORK
empower
The highlighting commitment of this paper is
developments that upgrade the personal satisfaction, yet
that the use of IP lookup algorithm with the base of Fog
it produces exceptional measures of information that are
Computing. In this project, we propose very small
troublesome for customary frameworks, the cloud, and
servers known as “Nano data centers” or “Nano data
even edge registering to deal with. fog processing is
servers” abbreviated as (NDC) which play the role of
intended to defeat these impediments.12].
fogs sited in end-user premises for running the applications in a point-to-point fashion. We use a single
I] Fog – Feasibility
device e.g. a laptop or a desktop which plays the role of
As billions of gadgets get associated with the Internet, it
“Main data center” or “Main data server” which is
won't be manageable to utilize the cloud as a
specifically the centralized data server of our entire
concentrated server. The path forward is to decentralize
system.With the entire setup from cloud server to the
calculations far from the cloud towards the edge of the
end user devices connected in a hierarchical manner we
system nearer to the client.[13] This decreases the
try to identify various use cases in which the energy
idleness of correspondence between a client gadget and
consumption of the NDCs and MDCs are calculated
the cloud, and is the commence of 'mist processing'
based on the algorithm implemented “EC Computation
characterized in this paper. The point of this paper is to
Algorithm”.
highlight the practicality and the advantages in
Main Datacenter: Main Data center is the server where
enhancing the Quality-of-Service and Experience by
the applications are deployed. We show the MDC
6 IDL - International Digital Library Of Technology & Research Volume 1, Issue 6, June 2017
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configurations, load status, amount of energy it can consume in idle state, current state, no. of connections associated etc. If the Nano Data center threshold limit (maximum number of request can be handle) exceed, then the Main data center will process the request. It accepts requests redirected from all sources i.e different fogs It has the maximum threshold limit compares to the Nano Data Centers. E.g. Any localhost or cloud server browsed on any device such as laptops or desktops. Nano Datacenter: Nano Data center is the server where
Fig 2: System Architecture of Fog Computing.
the applications are deployed. If the IP address belongs to the region of Nano Data center and if the threshold limit is not exceeded then the Nano data center will process the client request, else it will be redirect the request to the Main Data center. Nano data centers have limited capacity. We calculate various parameters like MDC as mentioned above. It processes the requests until the limit exceeded and then redirects to the MDC as soon as its status turns from normal to overloaded. It has the low threshold limit compares to the Main Data Centers. E.g.
End-user
and diminish the degree of information related to the cloud for handling, examination and capacity. This is regularly done to enhance productivity; however, it might similarly be utilized for security and consistence reasons. Prominent fog processing applications incorporate savvy framework, shrewd city, keen structures, vehicle systems and programming characterized systems. The illustration fog originates from the meteorological term for a cloud
devices,
mobile-applications,
web-
applications, geo-satellite requesting device, location detector,
The main purpose of fogging is to augment productivity
raspberry-pi
toolkit,
etc.
end of the ground, similarly as fog focuses on the end of the system. With the help of this implementation we can see the realtime energy computations and power consumptions taking place basically in 2 scenarios:
1) System with 1 MDC and 2 NDCs The entire system consisting of a centralized data center and the Nano-data centers relying on main data center. The various parameters are calculated such as
7 IDL - International Digital Library Of Technology & Research Volume 1, Issue 6, June 2017
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Pmax, Cmax, current load for the MDC and NDCs
which utilizes the datagram's goal route to decide for
respectively.
each datagram the following hop, is along these lines vital to accomplish the datagram sending rates required.
2) System when all NDCs are transformed to MDC.
Additionally, the bundle may experience numerous
As soon as the threshold limit for the NDCs are hit the
switches before it achieves its goal. Consequently,
NDC are shifted as the MDC servers with the increased
diminish in postponement by small scale seconds brings
throughput and capacity. The entire system consisting of
about gigantic cut down in an opportunity to achieve the
a centralized data center as the nano-data centers
goal. IP address query is troublesome because it requires
working as MDCs. The various parameters are
a Longest Matching Prefix seek. Numerous query
calculated such as Pmax, Cmax, current load for all the
calculations are accessible to locate the Longest Prefix
MDCs
Matching; one such is the Elevator-Stairs Algorithm. Some top of the line routers has been actualized with
3.1 Algorithms and Techniques
equipment parallelism utilizing TCAM. In any case, TCAM is a great deal more costly regarding circuit
Energy consumption algorithm
multifaceted nature and in addition control utilization. In
IPlookup.
this manner, proficient algorithmic arrangements are
o
Modified Elevator Stairs Algorithm
Web Services.
basically required to be executed utilizing system processors as minimal effort and cost solutions. Among the state-of-the-art algorithms for IP address lookup, a binary search based on a balanced tree is
1) Efficient IP Lookup Algorithm.
effective in providing a low-cost solution. To construct a balanced search tree, the prefixes with the nesting
As there is heavy internet traffic, the backend
relationship should be converted into completely
supportive routers impose the capability of transmitting
disjointed prefixes. We propose Small balanced tree
the in-direction packets at high gigabits/second speed.
using entry reduction for IPLookup algo. Take the
The IP address lookup thus comes into its role of high-
specified IP address.
speed networks packets transmission to destined routers
Make respective segments.
To deal with gigabit-per-second movement rates, the
Take 1st segment and search at root level.
backend-supporting routers must have the capacity to
Speed Calculation – Array of the root level is
from source to destination. It is very challenging task.
forward a large number of datagrams every second on each of their ports. Quick IP address query in the routers,
considered and a lookup is applied directly. -
Consider the 1st segment node as 224.
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-
-
-
-
-
Work with only 1 subtree with root as
by wide number of users at distant locations.
224.
We make a war file of the respective application and
If the data is very dense, we consider
deploy it over the workspace and the local host
entire array in all other levels.
server of various devices.
Probably, we require a dictionary for the
In this case the devices can be different laptops,
sub-levels of a kind.
mobile devices, raspberry-pi kits etc. All these
If the next segment is 201, you look up
devices should be connected to a same LAN so that
201 in the dictionary for the 223 nodes,
the requests should not be disrupted.
Now your possible list of candidates is
3) EC Computation Algorithm.
just 64K items (i.e. all IP addresses that are 223,201.x.x). -
Repeat the above process with the next 2 levels.
-
The result is that you can resolve an IP address in just 4 lookups: 1 lookup in an array, and 3 dictionary lookups
This structure is also very easy to maintain. Inserting a new address or range requires at most four lookups and adds.
Same with deleting. Updates can be done in-place, without having to rebuild the entire tree.
Take care read and update should not come under same instance.
No concurrent updating should happen while concurrent read can be accessed.
2) Web Services. 4) Flow Chart of EC Computation Algorithm We make use of some basic runtime applications which make the request of the data centers as they are deployed to various systems. The Web services can be any system based application used remotely
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M_Energy 1 is the existing system. Nano data center can take the current load of only 11 Initially it can take amount of 5 units. (Pidle= 5 units). Totally incoming connections, it can take is 3, for each connection it takes the energy of 2 units. So, we are calculating the energy by using the formula;
Current Load = Pidle + CE = Pidle + Current Connection * E / Connection U Threshold is the total value for the energy it should be less than the current system. So, the Current load for the Servers are:
Current Load = Pidle + CE Nano1 = 5 + (3 * 2) = 11 Nano2 = 5 + (1 * 2) = 7 Main
= 50 + (100 *2) = 250
Fig : EC Computation Algorithm.
4.
RESULTS AND DISCUSSION.
NDCs consume a insignificant total of energy for around some apps by moving data in the vicinity to client side users and reducing the energy consumed over the transport network.
By the enhancement we can increase the values of the server as the main servers so in the second table we are using the PMAX is 600 for nano servers as well as main
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servers, but the current connections we can increase to
In this thesis, we studied, analyzed and brought out some
100 by the same initial energy.
results which discusses the outcomes of fog computing over cloud computing. We examined that the energy consumption of the Nano-servers called as fogs was considerably less when the applications are brought down at the networks edge at the client side devices. A detailed comparison of the energy consumed at the NDC level and MDC level was evaluated with the outcome revealing NDCs consumed less energy than MDCs for the same task performed for both scenarios
Current Load =Pidle + CE Main1 = 50 + (3 * 2) = 61 Main3 = 50 + (1 * 2) = 52 Main2 = 50 + (100*2) = 250
6.
FUTURE SCOPE.
Despite the commitments of the present theory in energy utilization of Cloud processing also, Fog registering, there are various open research challenges that should be handled with a specific end goal to further
Pmax is the maximum energy of the Nano server it is taken by: Pidle * Current Connection * E / Connection
propel the range. Furthermore, the wide range of applications fog can handle can be evaluated. Subsequently, as the vitality
Cmax is the maximum energy for the connection, it is given by: Pidle * E / Connection
5.
CONCLUSION
utilization displaying and estimation strategies proposed in this proposal can be connected to PaaS and IaaS, it is profitable to study vitality utilization of PaaS and IaaS in end-client terminals, transport system and server farms.
Cloud computing has become the base of the new trend
Besides, our outcomes depend on vitality utilization of
transforming our digital sector in IT. Large scale or
uses amid utilize stage and we did not consider vitality
small scale ir enterprise customers everywhere the cloud
utilization of uses and administrations in all their years.
services are grooming to the roots due to its wide range
Explore considering an existence cycle point of view
of abilities and advantages profiting the business
would be required to look at the aggregate ecological
revenue. Due to increase in demand of cloud based
impression of the applications and administrations.
storage, applications and services, the network traffic, energy consumption and routing are the upcoming major concerns.
11 IDL - International Digital Library Of Technology & Research Volume 1, Issue 6, June 2017
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7.
[5]. Cristea, V., Dobre, C., Pop, F.(2013) “Context-
ACKNOWLEDGMENT
aware environment internet of things.” Internet of I would like to express my sincere gratitude to my advisor Prof. Prabadevi B and Prof. Jayenti N Dept. SITE SCHOOL VIT UNIVERSITY, VELLORE for
Things and Inter-cooperative Computational Technologies for Collective Intelligence Studies in Computational Intelligence, vol. 460, pp. 25–49
continuous support for the research paper survey and analysis. I am very obliged for their supportive role and thankful for inspiring me to study more over this subject. I am very glad to have such experience of
research
[6]. Haak, D.(2010) “ Achieving high performance in smart grid data management.” White paper from Accenture
paper writing in a way to reach the concepts [7].Green cloud computing: Balancing energy in processing, storage, and transport (2011)
8.
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