AN ENHANCE LOAD BALANCING ALGORITHM FOR HETEROGENEOUS DATA CENTERS

AN ENHANCE LOAD BALANCING ALGORITHM FOR

HETEROGENEOUS DATA CENTERS

Authors: S.E.C. Nanayakkara, N.T. Mapa, D.S.A. Kandawala, H.P.P.A. Pemarathna

November2014

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Abstract

Load balancing is the process of distributing the load

among various nodes of a distributed system. The distribution

mechanism relies on underlying load balancing algorithm in

use. This concept called load balancing arises due to the fact

of requiring a proper resource utilization mechanism to gain

the maximum result and performance out of the resources

available. Many research projects have been conducted in this

field, but still it is in need of further updates because of

the rising user base and increasing need of services.

This project mainly focuses on the dynamic environmental

behaviors exist in the heterogeneous server farms to implement

a better performing novel load balancing algorithm. In advance

the algorithm follows a simple and concise flow of procedure

to generate the best possible decisions.

This novel algorithm is motivated by the significant

characteristics identified in both the Round Robin algorithm

(RR) and Weighted Round Robin algorithm (WRR). A comparative

analysis between other extended version of Round Robin

algorithms and Modified RR is included in this report as to

clearly clarify the significant advantages. A detailed

analysis of associated qualitative parameters namely Overall

Response time, Data Center Processing time and Grand total

cost was taken into consideration as performance metrics. All

the associated test results were generated and analyzed using

the java based simulator called Cloud Analyst which provides

necessary features and tools. Furthermore this report contains

the directions for future works that can be done related to

this project.

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The ultimate goal of this project is to implement an

innovative and enhanced load balancing algorithm for

heterogeneous data centers while show casing it stands ahead

of other algorithms with clear statistical and figurative

evidence by the use of test results obtained after application

of the algorithm on a simulated environment.

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Ackn ow ledgemen ts

First and foremost, we would like to thank the

supervisor in charge of this project, Mr. Lahiru

Ariyasinghe and the Head of Department Mr. Anuradha

Jayakody for the valuable guidance and advices. They

inspired us greatly to work in this project. Their

willingness to motivate us contributed tremendously to

our project. We also would like to thank them for showing

us some example that related to the topic of our project.

Besides, we would like to thank the Department of

Computer Systems And Networking for providing us with a

good environment and facilities to complete this project.

Also, we would like to take this opportunity to thank to

both universities for offering this subject, Computer

Technology Project.

Finally, an honorable mention goes to our families and

friends for their understandings and supports on us in

completing this project. Without helps of the particular

that mentioned above, we would face many difficulties

while doing this project.

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Table Of Contents Declaration................................................................................................................................i Abstract....................................................................................................................................ii Acknowledgements.................................................................................................................iii List Of Figures.........................................................................................................................v List Of Tables........................................................................................................................ vii1. INTRODUCTION.......................................................................................................... 11.1 Problem to be addressed........................................................................................... 11.2 Background Context................................................................................................. 11.3 Research Gap........................................................................................................... 51.4 Research Question.................................................................................................... 6

2. LITERATURE REVIEW.............................................................................................. 72.1 Round Robin with Server Affinity[1]...................................................................... 7

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2.2 Fair Round Robin[2]................................................................................................ 82.3 Efficient & Enhanced Algorithm............................................................................. 9

3. METHODOLOGY....................................................................................................... 103.1 Calculating Weights for Virtual Machines............................................................. 113.2 Prioritizing the Virtual Machines........................................................................... 123.3 Checking the Availability........................................................................................ 13

4. RESEARCH FINDINGS............................................................................................. 145. RESULTS & DISCUSSION........................................................................................ 155.1 Cloud Analyst Simulator[4]................................................................................... 155.2 Performance Metrics.............................................................................................. 165.3 Testing Procedure.................................................................................................. 175.4 Test Results............................................................................................................ 185.5 Discussion on Generated Results........................................................................... 24

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6. CONCLUSION AND FUTURE WORKS................................................................. 25REFERENCE ....................................................................................................................... 27

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List Of Figures

Figure 2:1:1 Algorithm Comparison ...................................................................7

Figure 2:2:1 Round Robin Algorithm[2]...............................................................8

Figure 2:2:2 Fair Round Robin Algorithm[2]........................................................8

Figure3:1 Four main functions of the Modified Round Robin algorithm...........................................................................................................10

Figure3:1:1 The Equation for calculating the weight.........................................11

Figure3:2:1 Virtual Machine List........................................................................12

Figure3:2:2 weight1[] array...............................................................................12

Figure3:2:3 weight2[] array...............................................................................12

Figure3:2:4 Orderarr[] array..............................................................................12

Figure3:2:5 Java code for prioritizing the VirtualMachines...........................................................................................................13

Figure3:3:1 Pseudo code for Checking the

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Availability.........................................................................................................13

Figure4:1 Flow chart for the Functionality of the Modified Round Robin LoadBalancing Algorithm..........................................................................................14

Figure5:1:1 Cloud Analyst Main Window..........................................................15

Figure5:1:2 Cloud Analyst Main ConfigurationWindow............................................................................................................16

Figure5:3:1 Summary of the Testing Procedure...............................................17

Figure5:4:1 User Base Parameter List across the regionsof theGlobe................................................................................................................18

Figure5:4:2 The Data Center and VM Settings used forthe simulation........................................................................................................18

Figure5:4:3 Main Configuration for Scenario1 ...............................................19

Figure5:4:4 Main Configuration for Scenario 2................................................19

Figure5:4:5 Result of Overall Response Time Compared with ServerAffinity.............................................................................................................20

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Figure5:4:6 Result of Data Center Processing Time Compared with ServerAffinity......................................................................................................................20

Figure5:4:7 Main Configuration for Comparison with Fair RoundRobin........................................................................................................................21

Figure5:4:8 Result of Overall Response Time Compared withFair RoundRobin........................................................................................................................21

Figure5:4:9 Result of Data Center Processing Time Compared with Fair Round Robin.................................................................................................................................22

Figure5:4:10 User Base wise Overall Response Time Compared toRR.............................................................................................................................22

Figure5:4:11 User Base wise Data Center Processing Time Compared toRR............................................................................................................................23

Figure5:4:12 User Base wise Grand Total Cost Compared toRR............................................................................................................................23

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List Of Tables

Table 1:2:1 Literature Survey on existing Load Balancing Algorithms..........................3

Table 1:2:2 Literature Survey on existing Load Balancing Algorithms 2nd stage..........5

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1.INTRODUCTION

1.1 Problem to be addressed

With the consistently growing popularity of cloud and

number of cloud service providers, it is obviously most

significant to upgrade the cloud services which are

provided by cloud service providers by improving the

performance. Hence to satisfy the requirements regarding

bandwidth, memory requirements and response time of the

user base request can be done by implementing efficient VM

Load Balancing policies.

Currently there are only a limited number of VM load

balancing policies available and also follows Sophisticated

and complicated techniques to balance the load among

virtual machines in a data center. Hence this project

introduces an appropriate solution which addresses on

enhancing a proper load balancing algorithm by considering

in hand of simplicity, reliability and practical

aspects.This problem statement leads to the broad

objectives that are summarized below.

Evaluate and analyze the different existing loadbalancing algorithms and enhance a selected algorithmto implement a novel version which is capable ofhandling for heterogeneous data centers.

To implement the algorithm on Cloud Analyst and to analyze its performance

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Comparison of the proposed algorithm with the existingalgorithms on identified parameters

The rest of the sections more concern to satisfy theabove mentioned objectives.

1.2 Background Context

This section mainly includes the literature survey

analysis and related work done related in the field of load

balancing. In this stage the main focus was to compare and

contrast the different load balancing algorithms that has

been evolved throughout past years and to conduct in-depth

and clear view analysis of those to choose a better path

regarding this proposed project. The in detailed

information about the conducted analysis is summarized as

follows.

Algorithm Paper

Drawbacks Advantages

ESCE Algorithm

Analysisof loadbalancingalgorithm incloudcomputing &study of game

Fault tolerance low Throughput,response time,overhead in alower levelcompared to the

otherdynamicEfficient

loadbalancingalgorithm incloud environment

The jobs areequally spread,thecomplete,computingsystem isloadbalancedand numberof VMs areunderutilize. Hence

A survey onloadbalancing algorithms in

Single point of failure

Efficient andenhancedalgorithm in

Additional computation overhead to scanqueueagain and again

Weighted least connectionAlgorithm

Anefficientloadbalancingscheme forheterogeneousprocessingsystem

Evaluate node load balancing by measuringof connect quantity, the method is not perfect. There are otherparameters that affect

May allocate the taskforeachnodeaccordingtoconnectionquantity

Show the An adaptive

weightleast

The load status of servers is not

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algorithm based onserver clusters

status can’t be revealedby the number of links only.

The weightdescription is

inaccurate.In traditionalload balancingalgorithm,server weight issupposedas a

Ant colony optimization Algorithm

Antcolonyoptimizationfor effectiveloadbalancing incloudcomputing

Networkoverhead becauseof the largenumber of ants.

Onlyavailabilityof node is beenconsidered.

Nodes statuschange after

Optimum solution ofload.

No singlepoint of failure.

Honey bee inspired load balancing Algorithm

A survey onloadbalancing incloud

Low priority load become staycontinuously

Maximizing the throughput

Low overheadLoad

balancing incloud

Throughput is not increased with an

ThrottledAlgorithm

Comparativeanalysis of existing dynamic load

When requests increase beyond threshold valuewaiting timewill beincreased as the

Better response time.

Round RobinAlgorithm

Loadbalancing ofweb serversystem using

Does not consider about the current load andresponsiveness Efficient

loadbalancingalgorithm in

Fast and easy to implement.

Table 1:2:1 Advantages and disadvantages of some of the existing load balancing algorithms

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After performing the above analysis the initial aim was to

focus on developing an enhanced load balancing algorithm

while mitigating the errors and drawbacks identified during

the analysis to achieve better goals in server farms on any

open environment. Along with the literature survey few most

important and compulsory qualitative factors were

identified which should be considered towards better load

balancing. Further in depth details regarding the above

mentioned factors will be described in the sections to

come.

From the above literature survey according to the

gathered information this project proposes to enhance the

Round Robin (RR) load balancing algorithm. According to the

analyzed survey information it clearly highlights the Round

Robin algorithm as the efficient algorithm suited for a

light weight server environment compared to the otheralgorithms due to itssimplicity and reliability.

WLC Throttled Honey

BeeACO ESCE

CompareWith

RR

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Figure 1:2:1 Algorithm Comparison

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The second section directs a comparative analysis regarding

the work done related to this research area by the other

parties to enhance the Round Robin load balancing algorithmwith the same objective.

Algorithm Name

Description Plus Points DrawbacksRound Robinwith ServerAffinity [1]

Implemented usingHash maps and VMstate list.

Save the stateOf the previous allocation of a VM to a

Only consideredVMs with homogeneous

Fair RoundRobin (FRR) [2]

Provide fairness toexecuting nodes by considering

Considers thevariations of request size.

Only consideredVMs with homogeneous Efficie

nt &EnhancedAlgorit

Maintains VM indextable containing state of

Checking VMavailability& consideringexpected Response

Problem ofdeadlocks & Server overflow.

1.3 ResearchGap

Table 1:2:2 Survey on existing algorithms 2nd stage

According to the previous section there were drawbacks

and problems remaining with the existing and newly

researched and proposed load balancing algorithms. They can

be listed as follow

They assume that the virtual machines in a datacenter have homogeneous characteristics.

Deadlocks can be occurred due to actions of some loadbalancing algorithms.

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Some of the algorithms does not consider about the availability of the virtual machines in a datacenter.

The Modified Round Robin bridge the gap of these

drawbacks remaining with the existing load balancing

algorithms.

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1.4 Research Question

Due to rapid changes of networking environments, such

as increasing the user base, number of requests generated

and expected services from the servers. The load balancing

algorithms has to evolve and need to adapt the on-going

transformation. Hence implementing an applicable algorithm

still prevails in the research state.

Further questions may arise when existing algorithms

follow complex and mature techniques to balance the load

among networking data centers. There are various issues

while dealing with load balancing in a cloud computing

environment. Each load balancing algorithm must be such as

to achieve the desired goal. Some algorithms aims at

achieving higher throughput, some aims at achieving minimum

response time, some other aims to achieve maximum resource

utilization while some aims at achieving a trade-off

between all these metrics. Hence come up with an efficient

and accurate Load balancing still being required in this

field of research area.

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2. LITERATUREREVIEW

2.1 Round Robin with Server Affinity[1]

Basically this algorithm addresses the problem, which is

not saving the state of previous allocation of VMs to a

request from a given User base. If any request received

from same user base the algorithm must run again. That

clearly shows the increment of the response time and the

processing time of the request. As a convenient solution RR

with Server Affinity aims to reduce total response time and

processing time of user base requests by Enhancing Round

Robin Algorithm.

Implementation of the RR Sever Affinity is accomplished using hash map and a VM

statelist.

Hash Map: Used to store the entries for the last VM allocated to the request. VM State List: Stores the allocation status of each VM.

When a request comes from user base, if an entry for the

given user base exist in the hash map and if particular VM

is available in the VM state list, no need to run algorithm

again. It leads to reduce the response time and processing

time significantly. That has been confirmed by the

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following graph. The main fact about the algorithm is which

implemented only for homogenous networking environment[5].

Figure 2:1:1 AlgorithmComparison

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2.2 Fair Round Robin[2]

This algorithm identifies the existing algorithms as

complex and increase executing node's load which degrades

its performance. As a solution this algorithm enhances the

Round Robin algorithm by implementing dynamic time quantum

based on algorithm executing round. Hence this algorithm

follows quit simple strategies.

The procedure of this algorithm mainly concerned with fair

scheduling when the burst time of incoming request load is

having great variance. The overall algorithm execution

requires less number of rounds to follow thus the algorithm

when applied to large number of request performs faster

providing better response time to any request. Fair Round

Robin algorithm thus provides fairness to larger requests

and smaller one also to the load coming at executing node

in the cloud. Same as the Server Affinity this also

implemented for VMs with homogenous characteristics[2].

Following results shows the better performance of the FairRR algorithm than RoundRobin algorithm.[6]

Figure 2:2:1 Round Robin Algorithm[2]

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Figure 2:2:2 Fair Round Robin Algorithm[2]

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2.3 Efficient & Enhanced Algorithm

This algorithm also proposes an enhanced Round Robin

Algorithm by comparing the performance with existing

algorithm characteristics. Hence this directs a comparative

analysis to address as an efficient algorithm[3].

This algorithm follows the procedure, which is to find the

expected response time. To calculate the response time

following equation has been used.

Response Time = Fint - Arrt + TDelay ………..(1)

Where, Arrt is the arrival time of user request and Fint is

the finish time of user request and the transmission delay

can be determined using the following formulas;

TDelay = Tlatency + Ttransfer……. (2)

Where, TDelay is the transmission delay T latency is the

network latency and T transfer is the time taken to

transfer the size of data of a single request (D) from

source location to destination.[7]

Ttransfer = D / Bwperuser ……….. (3) Bwperuser = Bwtotal / Nr ……….. (4)

Where, Bwtotal is the total available bandwidth and Nr is

the number of user requests currently in transmission. The

Internet Characteristics also keeps track of the number of

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user requests in between two regions for the value of

Nr[3].

As a disadvantage this algorithm can be fall in to failures

like starvation and server overflow. Other than that VMs

contain equal characteristics which are known as homogenous

datacenters.[8]

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3. METHODOLOGY

Based on the literature survey that has been conducted,

this project came up with the modified algorithm which

addresses the most of the drawbacks remaining with the

existing load balancing algorithms. The proposed modified

load balancing algorithm is inspired by the Round Robin

algorithm and is concerning more towards the heterogeneous

characteristics of the Virtual Machines in a Data Center

such as memory, bandwidth and also the availability of the

Virtual Machines. The Modified Round Robin algorithm canbe mainly split into four functions as given below.

CalculatingWeights usingBandwidth and

Memory

Prioritize VMs

Check the Availability of the VMs

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Select the VMsin a

Round Robinmanner

Figure3:1 Four main functions of the Modified Round Robin algorithm

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First the all the algorithm calculates weights for allthe Virtual Machines in a Data

Center based on the VMs Bandwidth and Memory. And then it prioritizes the Virtual Machines according to

their weights. Higher weights gain higher priority. Then the algorithm checks for the availability of the Virtual Machines. And finally it will select the Virtual Machine based on the priority and the availability

3.1 Calculating Weights for Virtual MachinesAs mentioned earlier, weights are calculated for

all the Virtual Machines in a Data Center. Theweight was calculated by dividing the addition ofthe Bandwidth and Memory of the Virtual Machine bytwo into hundred.

B M W

2 *100Figure3:1:1 The Equation for calculating the weight

B = Bandwidth of the VM M = Memory of the VMW = Weight of the VM

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After going through many number of tests andsimulations, according to their test results, itshowed that Bandwidth and Memory has the same effecton the performance of a Virtual Machine. Hence theaverage value of the bandwidth and memory has beentaken. As Bandwidth and Memory variables store longvalues, to simplify the weight value further theaverage value of the addition of bandwidth andmemory has been divided by 100.

One of the main important point is that all thecalculate weights are stored in an array calledweight1[9].

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3.2 Prioritizing the Virtual Machines

The functionality of prioritizing the Virtual Machines can be presented in diagrams as shown below.

First the Virtual Machine List will be prepared according to the GUI inputs

VM List

Figure3:2:1 Virtual Machine List

Then according to the first function which has briefly explained earlier, all the calculated weights will be stored in the weight1[] array.

Weight1[]

Figure3:2:2weight1[] array

For an example assume that K2 > K0 > K1 > K3 As the next step the algorithm will take a copy

of the weight1[] array(weight2[]) and then weight2[] array will be

sorted in descending order.Weight2[]

Figure3:2:3 weight2[] array

And then by storing weight1[]s index of weight2[]s element, another array called orderarr[] will be prepared.

Orderarr[]

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Figure3:2:4 Orderarr[] array

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Finally the Orderarr[] array will contain all the indices of Virtual Machine

List in descending order based on their weights. The corresponding java code for this function can be shown as below.

for(int z=0; z < weight2.length; z++){

orderarr[z] = Arrays.asList(weight1).indexOf(weight2[z]);

}Figure3:2:5 Java code for prioritizing the Virtual Machines

3.3 Checking the Availability

This function mainly checks the availability of

the Orderarr[] arrays elements in a Round Robin

manner. For further explanation a pseudo code canbe presented as below.

Checking the availability of orderarr[currVM]th VM; Initially currVM = -1Loop{

currVM++ Availabilityof the

orderarr[currVM]

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VMif(availa

ble)select the

VM;else continue;

}

Figure3:3:1 Pseudo code for Checking the Availability

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The CurrVM variable is a global variable which

represents the indices of the Orderarr[] array.

4. RESEARCH FINDINGSSt

Calculate weightsfor alltheVMs

PrioritizeVMs

currVM++

YesIs

orderarr[currV M]th VM

Return

orderarr[currVM]

No

currVM++ St

currVM =0

YesIs

currVM

No

Figure4:1 Flow chart for the Functionality of the Modified Round Robin Load Balancing Algorithm

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5. RESULTS & DISCUSSION

5.1 Cloud AnalystSimulator[4]

After implementing the code and performing the necessary

modifications, the modified version of Round Robin load

balancing algorithm which is described in this report was

tested using a simulated environment. For the simulation

purpose Cloud Analyst simulator had been used which is

java based application tool including all the important

functionalities dedicated for testing load balancing

algorithms.[10]

It provides a simple and generic graphical interface

with all the options and settings needed to build a

simulated environment anyone desires. [11]

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Figure5:1:1 Cloud Analyst Main Window

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Figure5:1:2 Cloud Analyst MainConfiguration Window

5.2 Performance Metrics

Before conducting the testing phase three main performance

parameters were selected. The test results was analyzed

against the below listed performance metrics.

• Overall Response Time :Time between the submission of the request and the receipt if response.

• Data Center Processing Time :Time consumed to process the entire user request

• Cost Factor :This is calculated as the addition of both VM Cost

and Data Transfer Cost. Mainly the VM cost is

related with the memory consumption, storage

consumption and VM up time.

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VM Cost Data Transfer Cost

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5.3 Testing Procedure

The overall testing procedure can besummarized as shown below.

Figure5:3:1 Summary of the Testing Procedure

Mainly the testing procedure can be divided into 3

categories as mentioned below. (1) Comparison of

Server Affinity with Modified Code(2) Comparison of Fair Round Robin with Modified Code(3) Comparison of Generic Round Robin Code with Modified Code.

The Efficient and Enhanced Algorithm was taken as reference

to gather knowledge on how to perform a well-defined

testing flow and to learn on theories behind evaluating

load balancing algorithms. It was taken into account for

the comparative analysis as it is not a Pio Round Robin

based load balancing algorithm. But the other two

algorithms namely Fair Round Robin and Server Affinity is

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under consideration as both of them are Pio Round Robin

based load balancing algorithms.

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5.4 Test Results

(1) Comparison of with Modified Code

For this comparison two different scenarios was taken

into consideration. These scenarios described as

follows.

Scenario1 : Six User Bases with only one Data Center Scenario2 : Six User Bases with two Data Centers

Figure5:4:1 User Base Parameter List across the regions of the Globe

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Figure5:4:2 The Data Center and VM Settings used for the simulation

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Figure5:4:3 Main Configuration for Scenario 1

Figure5:4:4 Main Configuration for Scenario 2

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Figure5:4:5 Result of Overall Response Time Comparedwith Server Affinity

Figure5:4:6 Result of Data Center Processing TimeCompared with Server Affinity

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(2) Comparison of Fair Round Robin with Modified Code

All the necessary VM setting and test resultsare clearly include below.

Figure5:4:7 Main Configuration for Comparison with Fair Round Robin

Figure5:4:8 Result of Overall Response Time Comparedwith Fair Round Robin

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Figure5:4:9 Result of Data Center Processing Time Compared with FairRound Robin

(3) Comparison of Generic Round Robin with Modified Code

For this comparison the same VM setting and main

configuration settings which was used with the

comparison between Fair Round Robin was used. But to

make it more efficient and to generate more practical

related results the number of user bases was increased

gradually as to increase the amount of requests

directed towards the data center. This modification

was done to test the behaviour of the modified code

against extreme conditions.

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Figure5:4:10 User Base wise Overall Response Time Compared toRR

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Figure5:4:11 User Base wise Data Center Processing Time Compared to RR

Figure5:4:12 User Base wise Grand Total Cost Compared to RR

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5.5 Discussion on Generated Results

A clear look into both the comparisons performed with the

Server Affinity and Fair Round Robin load balancing

algorithms shows that the modified algorithm which is

thoroughly described throughout this report shows best

results with respect to Overall Response Time and Data

Center Processing Time.

The main reason for this outcome is that the modified code

always considers the ability or the power of the server

when directing the traffic towards them. But both the

Server Affinity and Fair Round Robin do not take the server

capabilities into consideration. And the other reason can

be stated as the simplicity of the algorithm which reduced

the computational power and leads to much lesser Data

Center Processing values. This concludes that the modified

algorithm performs better compared to the other extended

versions of the basic Round Robin load balancing algorithm.

Furthermore to make it clearer and to improve clarity of

the testing phase the final comparison performed against

the basic Round Robin shows favourable results towards the

modified Code. The reason that should be highlighted is

that using the Modified Code, the server capabilities and

server availability is taken into consideration. And it is

modified in a simpler way giving a twist to the generic

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Round Robin code which leads to the outcome of better

results.

As the final statement we can conclude that the Modified

Code achieved all is expected aspects and stand ahead of

the algorithms which are taken into consideration in this

report. Moreover the implementation flow of the Modified

code has opened different directions of enhancing the load

balancing algorithms to make it perform better.

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6. CONCLUSION AND FUTURE WORKS

Regardless of developments in the IT industry, Cloud

computing is an expanding computing paradigm which has

influenced in every field of entity in the globalize

industry whether it is in the public or private sector. Due

to the fast growing cloud importance, discovering new

methods to improve cloud service is an area of concern and

research focal point.

In current situation, there are only a limited number

of VM load balancing policies available. Besides that Cloud

Analyst implement the characteristics of VMs inside a data

center in equal manner which is an impractical solution for

a real world scenario. The research work engaged

developing and enhancing a Load Balancing algorithm for the

cloud environment and directing a comparative analysis of

proposed algorithm with existing proposed load balancing

algorithms. Along with the observations most of the

researches enhanced the performance of load balancing

algorithm for homogeneous VM data centers implement using

Cloud Analyst.

The major contribution of the proposed work in the field of

cloud computing is that the authors have introduced a

upgraded algorithm functionality, that is to change the

characteristics of VMs in a data center and come up with

an equation to change the VM characteristics. Further

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proposed algorithm provides the optimal and most specific

solution to allocate the work load among VMs to increase

the performance factors that is to say response time and

processing time in an efficient manner.

Without ending this research at this point, it can be

extend by doing further modifications. The proposed

algorithm can be upgraded by adding the energy awareness by

considering the available bandwidth, memory and other

necessary parameters dynamically. As the proposed algorithm

consider about the prioritized servers there can be

situations like server overflow and starvation. By solving

these problems this proposed algorithm can be turned in to

a more efficient and accurate load balancing algorithm.

Moreover this proposed algorithm was tested in a virtual

environment running in a simulator. As a new

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step the proposed algorithm can be tested in a real

environment. Moreover the failure handling mechanisms such

as handling exceptions can be added in to the simulation

when a server goes down for further enhancements.

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REFERENCES

[1] Komal Mahajan et al., “Round Robin with ServerAffinity: A VM LoadBalancing Algorithm for Cloud Based Infrastructure”, Dept.CSE & ICT, Jaypee Univ. ofInformation Technology, Waknaghat,India, July 2013.[2] Stuti Dave and Prashant Maheta, “Utilizing Round RobinConcept for Load BalancingAlgorithm at Virtual Machine Level in Cloud Environment”, BH Gardi College of Eng.& Tech., Gujarat,India, May 2014.[3] Tejinder Sharma and Vijay Kumar Banga, “Efficient andEnhanced Algorithm inCloud Computing”,Mar. 2013.[4] Bhathiya Wickremasinghe, “CloudAnalyst: A CloudSim-

based Tool for Modelling and Analysis of Large Scale Cloud

Computing Environments”, Dept. CSSE, Melbourne Univ., June

2009.

[5] (2014, Mar. 6). LoadBalancers [Online]. Available:https://wiki.gogrid.com/index.php/Load_Balancers#Load_Balancer_Algorithm[6] Shilpa S.et al., “Analysis of Load Balancing Algorithms

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Journal of Advanced Research in Computer Engineering

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2013

[8] Brajendra Kumar and Dr. VineetRichhariya. “Load

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[9] Xiaonian Tong and Wanneng Shu. “An efficient dynamicload balancing scheme forheterogeneous processing system”. ComputationalIntelligence and National computing,2009, 319-322.

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[10] David Quaid. (2013, Jun 06). Load Balancing Scheduling

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http://www.loadbalancerblog.com/blog/2013/06/load-

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