A Comparative Study between Honeybee Foraging Behaviour Algorithm and Round Robin Algorithm for...

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104/12/2023

A Comparative Study between Honeybee Foraging Behaviour Algorithm and Round

Robin Algorithm for Load Balancing in Cloud Environment.

Heaven’s Light is Our Guide

Rajshahi University of Engineering & Technology

Supervised By : Syed Tauhid Zuhori Lecturer ,Dept. of CSE. RUET.

Presented By :

Name:Tamanna shamrin Roll:073008Runia Tanbin Roll:073053

Thesis title:A Comparative Study between Honeybee Foraging Behaviour Algorithm and Round Robin Algorithm for Load Balancing in Cloud Environment.

Contents Objectives Related Works Introduction How does cloud like ? Cloud Architecture Theoretical Framework Advantages & Disadvantages Load Balancing Elaboration of abbreviations Block Diagram of load balancing in cloud environment Basic Steps Flow chart for load balancing (GUIC) for RR algorithm Flow chart for load balancing (GUIC) for Honeybee foraging behavior algorithm Flow chart for load balancing SA(services) Flow chart for load balancing TVM(request for checking of availability of VM) Flow chart for honeybee foraging behavior algorithm QoSC(service) Implementation Performance measure Limitation & Future Work Summary & Conclusions References

04/12/2023

A Comparative Study between Honeybee Foraging Behaviour Algorithm and Round

Robin Algorithm for Load Balancing in Cloud Environment.

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Objectives

• Study of Cloud Computing.• Study the performance of some of the existing Load Balancing

Algorithms.• To design and develop the concept of Load Balancing.• Use of Honeybee Foraging Behaviour Algorithm.• Use of Round Robin Algorithm.• Performance measure between two algorithms.

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Related Works

• November,2010,K.Mukherjee & G.Sahoo, Dept of CSE, Birla Institute of Technology Mesra,Ranchi, India, ”Green Cloud: An Algorithmic Approach”.

• 2010,Martin Randles,David Lamb,School of computing & Mathematical Sciences, Liverpool John Moores University,UK,” A Comparative study into distributed Load balancing Algorithms for cloud computing”.

• May,2011,Ram Prasad Padhy & P Goutam Prasad Rao, Dept of CSE,National Institute of Technology, Orissa, India.” Load Balancing In Cloud Computing Systems”.

• Mar-Apr.2012,Tanveer Ahmed & Yogendra Singh,University School of Information Technology M.Tech CSE,GGSIPU, Dwarka ,New Delhi,Analytic Study Of Load Balancing Techniques Using Tool Cloud Analyst.


Introduction What is cloud computing?• The cloud is a computing service that is capable to provide

massive computing or storage resources without the need to invest money.

• It aims at surmounting the Computational complexity and provides dynamically services using very large scalable and virtualized resources over the Internet.


How does cloud look like?

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Figure1: How does cloud look like?

A Comparative Study between Honeybee Foraging Behaviour Algorithm and Round Robin Algorithm for Load Balancing in Cloud Environment.

Cloud Architecture

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Three types of services in cloud computing

1. Software as a Service

2. Platform as a Service

3. Infrastructure as a Service

Figure2: Cloud Architecture


Theoretical Framework

Clouds can be divided into 3 categories :

• Public Clouds• Private Clouds• Hybrid Clouds

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Figure3:Types of cloud


http://en.wikipedia.org/wiki/File:Cloud_computing_types.svg

Advantages & Disadvantages

Advantages

• Lower software cost• Improve performance• Fewer maintenance issue• Instant software updates• Increased data safety

Disadvantages

• Requires a constant internet connection

• Doesn’t not work well in low speed connections

• May be slow• Limited features


Load Balancing

Load Balancing is a process of reassigning the total load to the individual nodes of the collective system.

It makes resource utilization effective and to improve the response time of the job.

The aim of load balancing algorithm is dynamic in nature which does not consider the previous state.


Load Balancing(cont’d)

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Figure4:Load Balancing in Cloud Computing


Load Balancing (cont’d)

Goals of Load Balancing• To improve the performance. • To have a backup plan in case of system failure.• To maintain the system stability.• To accommodate future modification in the system.



For load balancing two types of solution are applied :

1) Honeybee Foraging Behaviour Algorithm.

2) Round Robin Algorithm.



Honeybee Foraging Behavior Algorithm• Based on behavior of honeybees.• To maintain overloaded and under loaded CPUs.• Provides good quality of services.

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Figure 5: Honeybee behavior


A pictorial representation of Honeybee Foraging Behavior Algorithm

Load Balancing(cont’d)

Round Robin Algorithm:• In round robin algorithm, time slices are assigned to each

server in equal portions.• It works in circular order.• TVM receives several requests from several consumers and it

sends them to several servers one by one by using this algorithm.



Round Robin Algorithm

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Client5

Client1

Client4Client3

Client2Service Provider

Figure 6: Round Robin Algorithm


Elaboration of abbreviations

GUIC: Graphical User Interface for Consumer

SA: Service Accepter

TVM: Tracker of Virtual Machine

VM: Virtual Machine

QoSC: Quality of Service Checker

BS: Billing System

SEM: Service Execution Manager

VMTD: Virtual Machine Turn-on of Decider

SLA: Service Level Agreement


Block Diagram of load balancing in cloud environment

Consumer

Figure 7: Block Diagram of load balancing in cloud environment

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GUIC

SA

TVM

QoSC

SLA

BS

SEM

PCPM

VMTD

VM


Basic Steps

Some steps to solve load balancing problem:Step 1: At first, consumer would submit services to GUIC, from where service

is submitted to SA.

Step 2:SA decides whether to accept the service or not after getting information of Virtual Machine status from TVM.

Step 3: TVM keep tracks about the status of VMs .

Step 4: If service is accepted then it is assigned to QoSC, which fixes up the SLA and the price of the requested service after getting information from BS.

Step 5: After QoSC, services are submitted to SEM, which interacts with PCPM. PCPM interacts with VMTD and turns the required number of VMs

Step 6: Finally the active VMs(which are turned on), execute the services on CPUs of IaaS.


Flow chart for load balancing(GUIC) for RR algorithm

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Start

Consumer to GUIC

Call the SA(services)

Flag==0Can’t be

carried out

End

Flag return value from SA(services)

Request Service is accepted

VM Free?

Using RR Billing system


Yes

No

No

Yes

Flow chart for load balancing(GUIC) for Honeybee foraging behavior algorithm

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Start

Consumer to GUIC

Call the SA(services)

Flag==0

Can’t be carried

out

End

Flag return value from SA(services)

Request Service is accepted

Call the QoSC Billing system


Yes

No

Flow chart for load balancingSA(services)

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Start

Call TVM (request for checking of availability of VM)

Flag return value from TVM

Flag==0Return false(machine are

not available)

Return true (available)

End


Yes

No

Flow chart for load balancingTVM(request for checking of availability of VM)

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Start

Request send to VM

Starts to check the availability of free VM

Return true

End


Flow chart for honeybee foraging behavior algorithm QoSC(service)

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high==low

High =high-1

Return true

Set upper threshold ->highSet lower threshold-> low

Start

End


Yes

No

Implementation

PC configuration:

Processor: Intel core i3

RAM:4GB Operating System : Windows 7 Language: C#


Performance measureServers No. Response time(in ms)

Honeybee Foraging Behavior Algorithm

Round Robin algorithm

0 234 187

1 218 171

2 202 156

3 187 140

4 171 124

5 156 109

6 140 93

7 124 78

8 109 62

9 93 46

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Table1: Response time of Honeybee Foraging Algorithm and RR algorithm for 10 servers


Performance measure(cont’d)

No. of Servers Average response time

HoneyBee Foraging Behaviour Algorithm

Round Robin algorithm

10 163.4 116.6

20 211.0 231.4

30 266.9 295.9

40 349.43 272.98

50 291.32 258.24

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Table2: Average response time of HoneyBee Foraging Algorithm and RR algorithm



0

50

100

150

200

250

300

350

400

10 20 30 40 50

Avg

res

pons

e ti

me

Server numbers

Honeybee

Round Robin

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Figure 8: Response time Vs Servers number graph


Performance measure(cont’d)Servers No. Bill pay

HoneyBee Foraging Behaviour Round Robin Algorithm

0 1 4

1 1 5

2 1 6

3 4 6

4 5 6

5 6 6

6 6 8

7 6 8

8 6 8

9 8 9

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Table3: Bill pay of HoneyBee Foraging Algorithm and RR algorithmA Comparative Study between Honeybee Foraging Behaviour Algorithm and Round Robin

Algorithm for Load Balancing in Cloud Environment.


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0

2

4

6

8

10

0 1 2 3 4 5 6 7 8 9

Honeybee

Round Robin

Figure 9: Graph of Bill Vs Servers number


Limitation & Future Work

• We create a cloud like environment . It does not work in real environment.

• In future we analysis another load balancing algorithm for comparison.

• Our system only work with job but in future we solve our problem for process.


Summary & Conclusions

• Cloud Computing is a vast concept and load balancing plays a very important role in case of Clouds.

• Cloud computing increases profitability by improving resource utilization.

• Costs are driven down by delivering appropriate resources.

• Cloud computing has enabled teams and organizations.• The performance can be increased by varying different

parameters.


References

[1] Anthony T.Velte, Toby J.Velte, Robert Elsenpeter, Cloud Computing A Practical Approach,TATA McGRAW-HILL Edition 2010.

[2] Martin Randles, Enas Odat, David Lamb, Osama Abu- Rahmeh and A. Taleb-Bendiab, ”A Comparative Experiment in Distributed Load Balancing”, 2009 Second International Conference on Developments in eSystems Engineering.

[3] Ali M. Alakeel, A Guide to Dynamic Load Balancing in Distributed Computer Systems, IJCSNS International Journal of Computer Science and Network Security, VOL.10 No.6, June 2010.

[4] Mequanint Moges, Thomas G.Robertazzi, ”Wireless Sensor Networks: Scheduling for Measurement and Data Reporting”, August 31, 2005

[5]K.Mukherjee & G.Sahoo,Dept of CSE,Birla Institute of Technology Mesra,Ranchi,India,”Green Cloud:An Algorithmic Approach”.

[6]Martin Randles,David Lamb,School of computing & Mathematical Sciences, Liverpool John Moores University,UK,” A Comparative study into distributed Load balancing Algorithms for cloud computing”.

[7]Ram Prasad Padhy & P Goutam Prasad Rao, Dept of CSE,National Institute of Technology, Orissa, India.” Load Balancing In Cloud Computing Systems”.


Thanks to All

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