Hierarchical Organization of Data Centre to Improve Quality of Services (QoS) by IJTEL

INTERNATIONAL JOURNAL OF TECHNOLOGICAL EXPLORATION AND LEARNING (IJTEL) www.ijtel.org

Hierarchical Organization of Data Centre to Improve Quality of Services (QoS) Pooja Devi

Kavita Rathi

Student at DCRUST, Murthal Gurgaon, India

Assistant Professor at DCRUST, Murthal Sonepat, India

Abstract— The cloud computing has become the buzz topic in today IT industry because it provides services at lower cost and high availability of services and supports the sharing of hardware and software simultaneously with multiple users. This paper exploits the coordination between data centre (DC) and user base (UB) to enhance the performance of data centre by determining the position of data centre relative to user base. Computing architecture is based on weak client (user base) and powerful data centre. Keywords- Cloud Analyst; resource- rich mobile computing.

Cloud computing;

Cloudlet;

INTRODUCTION

Cloud computing is getting advanced day by day, and now it has gain popularity in information services. Many companies such as Microsoft, Amazon, Red Hat, and IBM are investing cloud computing infrastructure and research. Cloud computing is a synthesis of computing services like software as a service (SaaS), platform as a service (PaaS), infrastructure as a service (IaaS) [2]. The purpose of cloud computing is to provide reliable, fault- tolerant, secure and scalable infrastructure for hosting internet based application services. On one hand there are cloud service providers who provide and manage the large scale computing infrastructure at a cheaper price and eliminating the higher cost of setting up application deployment environment and provide services in a very flexible manner. On the other hand many social networking sites and e-commerce application minimize cost and improve quality of services to the end users by taking benefits of such services. But bringing these two ends together there are several factors that will impact the net benefits such as distribution of user base, the available internet infrastructure, dynamic nature of user base etc[5]. Architecture of cloud computing is composed of resource rich data centre facilities and resource poor client’s background. Researchers and developers have analysed that we need to test the cloud environment before real time implementation but it is not easy to study the impact of several geographical factors. Cloud Analyst is a simulation technique to investigate such type of behaviour [5]. Another alternative is to use Cloud-Sim framework [4]. Cloud–Sim can be use to configure number of user bases, data

IJTEL, ISSN: 2319-2135, VOL.3, NO.3, JUNE 2014

centres, scheduling and allocation policies. By using CloudSim researchers and developers can test the newly developed application service in a controlled and easy to set up environment. II.

CLOUD ANALYST

Cloud computing and distributed system laboratory at Australia developed cloud analyst simulator with cloud sim functionality [1]. On the top of Grid- Sim Cloud- Sim is developed and on the top of Cloud-Sim Cloud Analyst is developed. A. New Features Some new features have been introduced in Cloud Analyst [3]. 

Application users: These are the autonomous entities that need to configure.



Internet: It is used for data transmission across internet.



Simulation defined by time period: In Cloud-Sim process takes place based on some predefine events. There is a need to generate events until the set time period expires.



Service brokers: Management of the routing of user requests based on the service broker polices like service proximity based routing, performance optimized routing, dynamically reconfiguring router.



GUI and ability to save simulation results: Cloud-Sim is GUI, it makes user to do configuration the simulation easy. User can save the simulation results in the form of pdf file for future use.

B. Components of Cloud Analyst [1]  Region: In cloud Analyst the world is divided into 6 Regions. 

User Bases: User base may be a single user or group of hundreds or thousands of users that are used to generate traffic and access cloud services.



Data Centre: Computing services are provided to user bases via data centre which process the user requests. If more than one data centre is available then service broker algorithm (closest data centre, optimum

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INTERNATIONAL JOURNAL OF TECHNOLOGICAL EXPLORATION AND LEARNING (IJTEL) www.ijtel.org response time and dynamically reconfigure router) is used to decide which data centre is used to process user request. 

VmloadBalancer: User generated request is first send to VmloadBalancer which is responsible to allocate the load based on load balancing policy (round robin, throttled, active monitoring load balancer) to the available VM.



Internet Cloudlet: It is a group of user requests containing information about size of request, size of input output files, originator and target application id.

III. RELATED WORK N. Bhargava et.al [1] in ”Performance Analysis of Cloud Computing for Distributed Client” by using cloud analyst analyse the data transfer cost, virtual machine cost and finally calculate the grand total cost and conclude that performance of data centre is effected by distance and number of user. Zia, Khan et.al [2] in “Scheme to Reduce Response Time in Cloud Computing Environment” explain that user and data centre in same region has low response time while user and data centre in different region has high response time and the data centre in hybrid data centre has average response time. B. Wickremasinghe et.al [4] explain how cloud analyst works, added features to cloud Sim and show that how cloud analyst can be used to model real world problem through a case study of social networking application facebook deployed on cloud. M. Malhotra et.al [5] in “Simulation for enhancing the Response and Processing time of Data Centre” identified that deploying two data centre instead of one reduces the response and processing time. So it is better to deploy two data centre instead of one data centre.

IV. PROPOSED MODEL Researchers have seemed that mobile hardware is resource poor relative to static client and server hardware. Memory size, battery life and speed are higher priorities to enhance the computation. Resource poverty is a major obstacle for application requiring processing and energy. Satyanarayan et.al [8] proposed a solution to mobile devices resource poverty is to use cloud computing. A mobile device can execute a resource intensive application on a distant high performance compute server and support thin client user interaction with the application over the internet. Proposed model state that weak client can transfer resource intensive application to cloudlet based on the distance of client to cloudlet. If a private cloudlet (data centre within the region of user) is present then the application will transfer to the cloudlet through Wi-Fi, otherwise application will be executed by public cloud which is connected to weak client through 3G. A. Advantage of using Wi-Fi connection  Energy saving 

Fast computation i.e. reduce response time



Better user experience.



Using the cloudlet also simplifies the challenge of meeting the peak bandwidth demand of multiple users.

B. If weak client and cloud are connected through 3G  As distance between client and cloud increases more energy is consumed because data travel through 3G 

For some times CPU sit idle.

S. Mohapatra et.al [6] in ”Comparision of Four Popular Heuristic for Load Balancing of Virtual Machines in Cloud Computing” explain the static and dynamic algorithm of load balancing policies. Compare the static policies Round Robin processing, Throttled scheduling process, Equally spread current execution load and First come first serve scheduling process with metrices like response time , processing time and total cost to fulfil the request. Finally conclude Round Robin algorithm best for performance. Ali Naser et.al [7] “An Efficient Load Balancing Algorithm for Virtualized Cloud Data Centre” analyse the performance of Throttled load algorithm with closest data centre, with optimized response time and with reconfiguring dynamically w.r.t cost, response time, processing time and showed that Throttled service broker is best chosen algorithm with better results in response time and processing time. Satyanarayanan et.al [8] “The Case for VM Based Cloudlets in Mobile Computing” explain the VM (virtual machine) architecture , in which a mobile user customized service software on a nearby cloudlet and then uses that services over a wireless LAN, mobile devices functions as a thin client relative to server. This strategy is known as cloudletbased, resource- rich mobile computing.

IJTEL, ISSN: 2319-2135, VOL.3, NO.3, JUNE 2014

Figure 1. Architecture of Cloudlet based Computing

V. EXPERIMENT SETUP Facebook is one of the social sites getting benefits of cloud computing with 200million registered users worldwide. According to survey of 18/06/2009 distribution of the facebook user base across the globe [4]:

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INTERNATIONAL JOURNAL OF TECHNOLOGICAL EXPLORATION AND LEARNING (IJTEL) www.ijtel.org TABLE I.

DISTRIBUTION OF THE FACEBOOK USER BASE ACROSS THE

Europe

Users

Asia

80 million

Africa

20 million

Ocenia

GLOBE

Region

Cloud Analyst Region Id

North America

South America

60 million 27 million 5 million 8 million

Here we have analysed the 1/100 scale of facebook TABLE II.

6 USER BASES PARAMETERS REPRESENTING 6 ABOVE MENTIONED REGIONS.

User Base

Region

Time Zone

Peak Hours (Local Time)

Peak Hours (GMT)

Simultaneous Online users During Peak Hrs

UB1

GMT-6.00

7.00-9.00 pm

13:00-15:00

40,000

UB2

GMT-4.00

7.00-9.00 pm

15:00-17:00

10,000

UB3

GMT+1.00

7.00-9.00 pm

20:00-22:00

30,000

UB4

GMT+6.00

7.00-9.00 pm

01:00-3:00

15,000

UB5

GMT+2.00

7.00-9.00 pm

21:00-23:00

5,000

UB6

GMT+10.00

7.00-9.00 pm

9:00-11:00

8,000

TABLE III.

COST CONFIGURATION

TABLE VI. $0.10

Cost per VM per hour(1024 Mb,100MIPS)

$0.10

Cost per Gb of data transfer(from/to Internet)

TABLE IV.

Simultaneous Online users During Off-Peak Hrs

4,000 1,000 3,000 1,500 500 800

BANDWIDTH MATRIX VALUES (MBPS)

Region/ Region

2000.0

1000.0

800.0

1000.0

2500.0

1000.0

1500.0

1000.0

500.0

1000.0

DATA CENTRE CONFIGURATION

VM image size =1000

VM memory= 1024Mb Data centre architecture =X86

VM bandwidth =1000 Data centre operating system= Linux

Data centre VMM = Xen memory per machine =2048Mb

Number of machine= 20

available bandwidth per machine =10000,

Storage per machine= 100000Mb Number of processor per machine= 4

5 1000.0 1000.0 1000.0 1000.0 1000.0 2000.0

processor speed =1000 MIPS user grouping factor =1000

VM policy =Time shared Request grouping factor =100 TABLE V.

user.

executable instruction per length = 250

LATENCY MATRIX VALUES (MILLISECOND)

Region/Region

25.0

100.0

150.0

250.0

100.0

25.0

250.0

500.0

350.0

200.0

150.0

250.0

25.0

150.0

200.0

250.0

500.0

150.0

25.0

500.0

250.0

350.0

150.0

500.0

25.0

500.0

100.0

200.0

500.0

25.0

IJTEL, ISSN: 2319-2135, VOL.3, NO.3, JUNE 2014

Scenario 1: In this scenario we have considered the 6 user bases with single data centre (present only one region at a time) in region 0, region 1, region 2, region 3, region 4 and region 5. The table given below show the response time of user bases according to the position of data centre in region 0, region 1, region 2, region 3, region 4, region 5 respectively.

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TABLE VII.

RESPONSE TIME OF 6 USER BASE AFTER SIMULATION BY PLACING DATA CENTRE IN EACH REGION RESPECTIVELY.

DATA CENTRE REGION

RESPONSE TIME of UB0

RESPONSE TIME of UB1

RESPONSE TIME of UB2

RESPONSE TIME of UB3

RESPONSE TIME of UB4

RESPONSE TIME of UB5

3087.52

1699

2626.4

2106.7

4820.65

5719.24

3824.81

153.37

2856.6

2599.2

5042.98

5964.65

3335.53

1982.83

2347.24

1876.82

4640.6

5959.57

3503.7

2449.51

2628.35

1684.91

5318.93

6503.96

3519.19

2121.74

2608.07

2605.14

4387.59

6521.04

3220.04

1852.62

2777.28

2637.47

5304.43

5681.6

7000 6000 5000 4000 3000 2000 1000 0

region 0 1 2 3 4 UB0 UB1 UB2 UB3 UB4 UB5

We can analyse from the graph that



UB0 has least response time when data centre is in region 0 as compared to when data centre is in region 1, 2, 3, 4, 5 respectively. UB1 has least response time when data centre is in region 1 as compared to when data centre is in region 0, 2, 3, 4, 5 respectively



UB2 has least response time when data centre is in region 2 as compared to when data centre is in region 0, 1, 3, 4, 5 respectively.



UB3 has least response time when data centre is in region 3 as compared to when data centre is in region 0, 1, 2, 4, 5 respectively.



Case 2.1: Single data centre: In this approach application is deployed at a single location, here it is at region 0 i.e at North America. After simulation table given below shows the average, min, max response and processing time TABLE VIII.

Figure 2. User base response time (milliseconds) according to position of single data centre in a region



Scenario 2: User base and data centre configuration is same but here we have varied the number of data centre from one to four to observe the performance.

UB4 has least response time when data centre is in region 4 as compared to when data centre is in region 0, 1, 2, 3, 5 respectively. UB5 has least response time when data centre is in region 5 as compared to when data centre is in region 0, 1, 2, 3, 4 respectively.

IJTEL, ISSN: 2319-2135, VOL.3, NO.3, JUNE 2014

OVERALL RESPONSE TIME SUMMARY

Avg (ms) Overall Response time: Data Centre Processing time: TABLE IX. Data Centre DC1

Min (ms)

Max (ms)

4252.81

263.09

15170.60

3861.29

13.14

14715.05

DATA CENTRE REQUEST SERVICING T IME Avg (ms) 3861.29

Min (ms) 13.14

Max (ms) 14715.05

Case 2.2: Two data centre: In this approach application is deployed at two locations but within the same region, here these are at region 0 i.e at North America. After simulation table given below shows the average, min, max response and processing time. TABLE X.

OVERALL RESPONSE TIME SUMMARY

Overall response time: Data Centre processing time: TABLE XI. Data Centre DC1 DC2

Avg (ms) 2313.78 1979.34

Min (ms) 232.13 13.14

Max (ms) 8160.86 7678.95

DATA CENTRE REQUEST SERVICING T IME Avg (ms) 1920.30 2032.69

Min (ms) 13.15 13.14

Max (ms) 7678.95 6787.86

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INTERNATIONAL JOURNAL OF TECHNOLOGICAL EXPLORATION AND LEARNING (IJTEL) www.ijtel.org Case 2.3: Three data centre: In this approach application is deployed at three locations but within the same region, here these are at region 0 i.e at North America. After simulation table given below shows the average, min, max response and processing time. TABLE XII.

Avg (ms) 1737.31

Data Centre DC1 DC2 DC3

1420.73

Min (ms) 107.97

Max (ms) 6013.18

13.14

5500.36

DATA CENTRE REQUEST SERVICING T IME

Avg (ms) 1450.29 1464.16 1348.65

Min (ms) 13.15 13.14 25.09

Max (ms) 5500.36 5122.78 4714.27

Case 2.4: Four data centre: In this approach application is deployed at four locations but within the same region, here these are at region 0 i.e at North America. After simulation table given below shows the average, min, max response and processing time. TABLE XIV.

OVERALL RESPONSE TIME SUMMARY

Overall response time: Data Centre processing time: TABLE XV.

Avg (ms) 1524.65 1214.06

Min (ms) 142.91 13.14

Max (ms) 6004.21 5504.65

DATA C ENTRE REQUEST SERVICING TIME

Data Centre

Avg (ms)

Min (ms)

Max (ms)

DC1

1220.34

13.14

4286.23

DC2 DC3 DC4

1177.69 1148.22 1313.90

13.15 46.98 25.09

4669.59 3269.44 5504.65

TABLE XVI.

RESPONSE TIME AND PROCESSING TIME OF DATA CENTRE WHEN THE NUMBER OF DATA CENTRE INCREASES.

Number of Data centre

Response time (millisecond)

processing time (millisecond)

4252.81 2313.78 1737.31 1524.64

3861.29 1979.34 1420 1214.06

DC1 DC2 DC3 DC4

4000

response time(milliseco nd)

3000 2000

processing time (millisecond)

1000 0 DC1

Figure 3 shows that bringing services closer to user bases reduce the response time (improves the quality of service).



Figure 4 shows that we can improve quality of service (response time and processing time) by increasing the number of data centre.



Energy saving as response and processing time reduces.



Fast computation i.e .reduces response time.



Better user experience.



Using the cloudlet also simplifies the challenge of meeting the peak bandwidth demand of multiple users.

VII. CONCLUSION Companies all over the world are financing or utilizing the services of cloud computing anywhere and at anytime. Cloud computing consist of thousands of data centre. Resources and services change with user requirements and environments. Data centre services, users and process are significant for availability and better quality of services. This research concludes that hierarchical representation of data centre leads to better user experience and save energy by bringing services closer to user bases that reduce the response time (improves the quality of service) and also analyze that we can improve quality of service (response time and processing time) by increasing the number of data centre. REFERENCES [1]

[2]

[3]

[4]

[5]

5000

DC2

DC3

DC4

OBSERVATION



OVERALL RESPONSE TIME SUMMARY

Overall response time: Data Centre processing time: TABLE XIII.

VI.

[6]

[7] [8]

Dr. Neeraj Bhargava, Dr. Neetu Bhargava International journal of Computer Science and Mobile Computing, ”Performance Analysis of Cloud Computing for Distributed Client”. IJCMC, vol. 2, issue 6, june2013, ISSN 2320-088X. Ashraf Zia, M.N.A. khan IJ Modern Education and Computer Science, ”A Scheme to reduce response time in cloud computing environment”. Published online july2013 in MECS.DOI: 10.5815/ijmecs.2013.06.08. Dhaval Limbani, Bhavesh Oza/ International journal of Engineering Research and Applications IJERA, ”Proposed Service Broker Strategy in Cloud Analyst for Cost Effective Data Centre Selection”. Bhathiya Wickremasinghe, Rodrigo N. Calheiros, and Rajkumar Buyya,” Cloud Analyst: A cloudSim-based Visul Modeller for Analysing Cloud Computing Environments and Application”. Manish Malhotra Intrenational journal of computing and corporate research,” A Simulation for Enhancing the Response and Processing Time of Data Centre” volume 1issue 3 manuscript 8 November 2011. Subasish Mohapatra, K Sumruti Rekha, Subhadarshini Mohanty, ”Comparision of Four Popular Heuristic for Load Balancing of Virtual Machines in Cloud Computing” International journal of computer applications(0975-8887) Volume 68-No. 6, April 2013. Ali Naser Abdulhussein, Jugal Harshvadan Joshi, “An Efficient Load Balancing Algorithm for Virtualized Cloud Data Centre”. Mahadev Satayanaranan, Paramvir Bhal, ”The Case for VM Based Cloudlets in Mobile Computing” published by IEEE CS 15361268/09/2009 IEEE.

Figure 3. Response time, Processing time VS number of data centre

Graph shows that increasing the number of data centre decreases the response time and processing time. IJTEL, ISSN: 2319-2135, VOL.3, NO.3, JUNE 2014

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