Different Strategies for Load Balancing in Cloud Computing Environment: a critical Study

7/26/2019 Different Strategies for Load Balancing in Cloud Computing Environment: a critical Study

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International Journal of Scientific Research Engineering & Technology (IJSRET), ISSN 2278 0882

Volume 3 Issue 1, April 2014

Different Strategies for Load Balancing in Cloud

Computing Environment: a critical Study

Amandeep1, Vandana Yadav2, Faz Mohammad3

1,2

Dept. of CSE, Galgotia University, G.Noida3Asst. Prof., Dept. of CSE, MAIT,Ghaziabad

AbstractTwenty first century is known as the era of technology. In

the technology of computing it is an era of cloud

computing. Cloud computing is the most recent

technology in today's world of computing and its become

more popular day by day due to its great feature (resource

pooling, rapid elasticity, scalability, efficiency and on

demand service). Cloud computing is built on the base of

distributed computing, grid computing and virtualization.

Cloud computing is defined as a large scale distributedcomputing paradigm that is driven by economics of

scale in which a pool of abstracted virtualized

dynamically.

1. INTRODUCTIONCloud computing is a model for delivering information

technology services in which resources are retrieved from

the internet through web-based tools and applications,

rather than a direct connection to a server. Data and

software packages are stored in servers. However, cloud

computing structure allows access to information as long

as an electronic device has access to the web. This type of

system allows employees to work remotely.

Cloud computing is so named because the information

being accessed is found in the "clouds", and does not

require a user to be in a specific place to gain access to it.

In Gartners report [2], considered cloud computing as

the first among top 10 most important technologies and

with a better prospect in successive years by

companies and organization and cloud will change to the

IT industry.

Cloud computing is a term, which involves virtualization,

distributed computing, networking, web services and

software. A cloud consists of scalable, managed

computing power, storage, platform and service are

delivered on demand to external customer over the

internet. The services are available to user in pay-per-use-

on demand model. Due to its great feature it is adopted

by all type of user like industry, organization and

institutional etc. There are many existing issues in cloud

computing like load balancing, virtual machine

migration, security ,energy management and server

consolidation etc. the increase in web traffic and

different service are increasing day by day making load

balancing a big research topic. in this paper we focus in

different type of load balancing algorithm.

Several elements are such as client, datacenters, and

distributed server [3].

2. DEFINITION OF CLOUD

COMPUTINGMany definitions are proposed for cloud computing

Here some of them are given here,

1) Cloud is a parallel and distributed computing system

consisting of collection of the interconnected and virtual

computers that are dynamically provisioned and

presented as one or more unified computing

resources based on the service-level- agreements

(SLA) that are established through the negotiationbetween the service provider and customer [4].

2) NIST definition of cloud computing- Cloud

computing is a model for enabling ubiquitousconvenient, on-demand network access to a shared poo

of configurable computing resources (e.g., networks

servers, storage, applications, and services) that can be

rapidly provisioned and released with minima

management effort or service provider interaction [5].

3) Cloud computing refers to both the applications

delivered as services over the internet and the

hardware and systems software in the datacenters tha

provide those services. The services themselves havelong been referred to as software as a service (SaaS), so

we use that term. The datacenter hardware and software

is what we will call a cloud [6].

Features of Cloud Computing


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1. Resource pooling: The providers

computing resources are pooled to serve multiple

consumers using a multi-tenant model, with different

physical and virtual resources dynamically assigned

and reassigned according to consumer demand.

2. Rapid elasticity: Capabilities can be elastically

provisioned and released, in some cases automatically,

to scale rapidly outward and inward commensuratewith demand. To the consumer, the capabilities

available for provisioning often appear to be

unlimited and can be appropriated in any quantity at

any time.

3. Scalability: Cloud computing provides resources

and services for users on demand. The resources are

scalable over several data centers. In order to achieve

a highly scalable system, balancing of the loads when

the load increases at a large extent and a cloud userdemands more resources online rapidly is very

important.

4. On-demand self-service: A consumer canunilaterally provision computing capabilities, such as

server time and network storage, as neededautomatically without requiring human interaction

with each

serviceprovider.

5. Efficiency:An efficient cloud computing system

should work for all the possible configurations ofcloud where users are requesting the resources on any

extent unknown to the cloud service providers

providing the important features like rapid elasticity

and high scalability with the much needed fault

tolerance. A proper distribution of tasks among theprocessors can achieve these features for the cloud

systems.

6. Dynamic Resource Allocation: Cloud computing

systems are allocating the loads across the system

either statically or dynamically. A dynamic resource

allocation policy proves to be better than the static one

to sustain the dynamic requirements of a cloud

user.

Cloud computing have three service model i.e. IaaS

(infrastructure as a service ), PaaS (platform as a

service) and SaaS (software as a service) and it have fourdeployment model i.e. public cloud , private cloud, hybrid

cloud and community cloud [6].

Load Balancing and Virtualization: Load Balancing:Any strategy for load distribution among the

computational element is known as load balancing.

Cloud balancing is a computer networking method to

distribute work load across multiple computer or a

computer cluster, network link, central processing units

disk drivers or other resources to achieve optima

resource utilization, maximize throughput, minimize

response time and avoid overload. Load balancing

helps in preventing bottleneck of system. It alsoavoiding a situation where some of the nodes are heavily

loaded while others nodes are ideal or doing very little

work. Without load balancing cloud computing would

very difficult to manage [7].

Virtualization:Virtualization means something which is not real bu

gives all the facility of a real. Virtualization is a

software implementation of a machine which wilexecute different program like a real machine.

Two type of virtualization found in case of cloud

computing.

1. Para Virtualization:Para virtualization means the hardware allow

multiple operating systems to run on single machine. It

also allows efficient use of system resources such as

memory and processor. In para virtualization all the

services are not fully available rather the services areprovided partially.

2. Full Virtualization:It means a complete installation of one machine is done

on another machine. It will result in a virtual machine

which will have all the software i.e. present in the actualserver. It helps to sharing a computer system among

multiple users. It provide emulating hardware on another

machine.[8]

Both virtualization and load balancing are closely

related. Some of the feature of cloud computing system

like fast response time, elasticity are achieved by load

balancing technique through virtualization.

Load Balancing Approach:Static and dynamic two type of load balancing

approaches use in cloud computing.

1. Static Approach:This approach is mainly defined in the design or

implementation of system. Static load balancing

algorithm divides the traffic equivalently between al

users. It uses only information about the average


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behavior of the system. These are much simpler and

ignore the current state or the load of the node in the

system.

2. Dynamic Approach:In this approach considered only the current state of the

system during load balancing decision. It is more suitable

for widely distributed system such a cloud computing

Dynamic approach has two parts

Centralized Approach: Only a single node is

responsible for managing and distribution within the

whole system.

Distributed Approach: Each node

independently builds its own load vector. Vector

collecting load information of other node. All decision is

made locally using local load vector.

Metrics for Load Balancing:

1. Throughput: - It is used to calculate the all tasks

whose execution has been completed. The performance ofany system is improved if throughput is high.

2. Fault Tolerance:It means recovery from failure. The

load balancing should be a good fault tolerant technique.

3. Migration time:It is the time to migrate the jobs or

resources from one node to other nodes. It should beminimized in order to enhance the performance of the

system.

4. Response Time:It is the amount of time that is taken

by a particular load balancing algorithm to response a task

in a system. This parameter should be minimized for betterperformance of a system.

5. Scalability:It is the ability of an algorithm to perform

Load balancing for any finite number of nodes of a

system.

Existing Load Balancing Algorithm:There are many load balancing algorithm which help to

achieve better throughput, improve the response time, high

resource utilization and better performance in cloud

computing environment [10, 19, 20, 21].

1. Task Scheduling Based On LB: This algorithmmainly consists two level task scheduling mechanism

which is based on load balancing to meet dynamic

requirements of users. It obtains high resource utilization.

This algorithm achieves load balancing by first mapping

tasks to virtual machines and then all virtual machines to

host resources .It is improving the task response time .It

also provide better resource utilization.[9]

2. Opportunistic Load BalancingOpportunistic load balancing is to attempt each node

keep busy, therefore does not consider the presen

workload of each computer. Opportunistic load

balancingassigns each task in free order to presen

node of useful .The advantage is quite simple and

reach load balance but its shortcoming is not considereach expectation execution time of task, therefore the

whole completion time is very poor.

3. Active clustering: Active clustering is used in

large scale cloud system. It provides bette

performance and high resource utilization. In this

algorithm same type nodes of the systems are grouped

together and they work together in groups. It works like

as self-aggregation load balancing technique where a

network is rewired to balance the load of the systemSystems optimize using similar job assignments by

connecting similar services. it improve the throughpu

by utilizing the increased system resource. It wildegrade as the system diversity increases.

4. Ant colony optimization: Ant algorithm is a

multi-agent approach to difficul

combinatorial optimization problem. In this algorithm

when the request is initiated the ant start it

movement. Movement of ant is of two ways:

Forward Movement: Forward Movement the an

moving in forward direction continuously moving from

one overloaded node to another node and check it is

overloaded or under loaded ,if ant find an over loadednode it will continuously moving in the forward

direction and check each nodes .

Backward Movement: If ants find an over loaded

node the ant will use the back ward movement to ge

to the previous node, in the algorithm if ant finds the

target node then ant will commit suicide, this algorithm

reduced the unnecessary back ward movement. It is

excellent in fault tolerance [11].

5. Shortest Response Time First:The idea of this

algorithm is straight forward. In this each process isassigned a priority which is allowed to run. In this equal

priority processes are scheduled in FCFS order. The

(SJF) algorithm is a special case of general priority

Scheduling algorithm. In SJF algorithm is priority is

the inverse of the next CPU burst. It means, if longe

the CPU burst then lower the priority. The SJF policy


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selects the job with the shortest processing time first. In

this algorithm shorter jobs are executed before long jobs.

In SJF, it is very important to know or estimate the

processing time of each job which is major problem of

SJF.

6. MIN-MIN Algorithm: It starts with a set of all

unassigned tasks .In this minimum completion time for all

tasks is found. Then after that among these minimumtimes the minimum value is selected. Then task with

minimum time schedule on machine. After that the

execution time for all other tasks is updated on that

machine then again the same procedure is followed until

all the tasks are assigned on the resources. The main

problem of this algorithm is has a starvation.

7. Honeybee Foraging Behavior: It is a nature

inspired Algorithm for self- organization. Honeybeeachieves global load balancing through local server

actions. The performance of the system is enhanced with

increased system diversity. The main problem is thatthroughput is not increased with an increase in system

size. When the diverse population of service types isrequired then this algorithm is best suited.[12]

8. A Fast Adaptive Load Balancing Method:This algorithm proposed a binary trees tructure that

is used to partition the simulation region into sub-domains. The characteristics of this fast adaptive balancing

method are to be adjusted the workload between the

processors fromlocal areas to global areas. the region

should be partitioned by using the binary tree mode.

There were partition line between the binary tree and theindexes of the cells on the left are smaller that of right and

the indexes on the top are smaller than the bottom.

Calculate the workload based on the balancing algorithm.

This algorithm has a faster balancing speed, less elapsed

time and less communication time cost of the simulation

procedure. Advantages are Relative smaller

communication overhead relative smaller communication

overhead, faster balancing speed, and high efficiency. It

cannot maintain the topology of cells [13].

9. Based Random Sampling: It is a distributed and

scalable load balancing approach that uses randomsampling of the system domain to achieve self-organization

these balancing the load across all nodes of system. This

algorithm is based on the construction of the virtual graph

having connectivity between the all nodes of the system

where each node of the graph is corresponding to the node

computer of the cloud system. Edges between nodes are

two types as Incoming edge and outgoing edge that is

used to consider the load of particular system and also

allotment the resources of the node. It is very good

technique to balance the load [14].

10. Max-Min Algorithm: Max-Min algorithm is

almost same as the min-min algorithm. The main

difference is following: In this algorithm first finding ouminimum execution times, then the maximum value is

selected which is the maximum time among all the

tasks on any resources. After that maximum time

finding, the task is assigned on the particular selected

machine. Then the execution time for all tasks i

updated on that machine, this is done by adding the

execution time of the assigned task to the execution times

of other tasks on that machine. Then all assigned task is

removed from the list that executed by the system.

11. Randomized: In this algorithm the process

allocation order is maintain for eachprocessor independent of allocation from remote

processor. This is static in nature. In this algorithm aprocess can be handled by a particular node n with a

probability p. When all the processes are of equa

loaded then this algorithm work well. Problem arises

when loads are of different computational complexities

This algorithm is not maintaining deterministic approach.

12. Lock-free multiprocessing solution for LB:It

proposed a lock-free multiprocessing load balancing

solution that avoids the use of shared memory in

contrast to other multiprocessing load balancing solutionswhich use shared memory and lock to maintain a user

session. It is achieved by modifying kernel. This solution

helps in improving the overall performance of load

balancer in a multicore environment by running multiple

load-balancing processes in one load balancer.

13. Heat Diffusion Based Dynamic Load BalancingIn this algorithm, proposed an efficient cell selection

scheme and two diffusion based algorithm called global

and local diffusion. According to heat diffusion

algorithm, the virtual environment is divided into a large

no of square cells and each square cells having object andevery node in the cell send load to its neighboring nodes

in every iteration and the transfer was the difference

between the current node to that of neighboring node

it is related to heat diffusion process. the advantages of

this algorithm are communication overhead is less, high

speed and require little amount of calculation.[15].


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14. Compare and Balance: This algorithm is uses to

reach an equilibrium condition and manage unbalanced

systems load. In this algorithm on the basis of probability,

current host randomly select a host and compare their

load. If load of current host is more than the selected

host, it transfers extra load to that particular node. Then

each host of the system performs the same procedure. This

load balancing algorithm is also designed andimplemented to reduce virtual machines migration time.

Shared storage memory is used to reduce virtual machines

migration time.

15. Resource Awareness Scheduling Algorithm:It is a combination of Min-Min and Max-Min algorithm

and have no time consuming instruction. The time

complexity of this algorithm is O(mn2) where m is the no.

of resources and n is the no. of task.

16. Connection Mechanism:It is a algorithm based onleast connection mechanism. It needs to count the no. of

connection for each server dynamically to estimate the

load. The load balancers record the connection no of eachserver. The no. of connection increase when a new

connection is dispatch to it and decrease the no whenconnection finished or timeout happen.[16]

17. Round Robin: In this algorithm all the processes are

divided between all processors. In this each process is

assigned to the processor in a round robin order. The work

load distributions between processors are equal. Differentprocesses have not same job processing time. At many

point of time some nodes may be heavily loaded and

others remain idle In web servers where http requests are of

similar nature and distributed equally then RR algorithm is

used .When time quantum is very large then RRScheduling Algorithm is same as the FCFS Scheduling.

and when time quantum is too small then Round Robin

Scheduling is known as Processor Sharing Algorithm.

18. Throttled Load Balancing Algorithm: It is

completely based on virtual machine. Client first

requesting the load balancer to check the right virtual

machine which access that load easily and perform the

operation that is given by the client.[17]

19. Load Balancing Using Firefly Algorithm: This

algorithm is inspired from firefly algorithm. The proposed

approach deals with a simulated cloud network with set of

requests and servers. The servers are associated with nodesand each node is supplied with some attributes. The

attributes are assigned to control the load in each node.

It has three levels.

1. Initially a population is generated from the cloud

network.

2. Scheduling index calculation

3. The scheduling list is optimized by firefly

algorithm.

It is efficient in optimizing scheduling by balancing the

load.[18]

3. CONCLUSION:

In this paper we discuss the load balancing in cloud

computing and metrics for load balancing in cloud

computing. We also discuss the cloud virtualization

Load balancing helps in proper utilization oresources and improve the performance of system. Load

balancing is a major issues in cloud computing. In this

paper we examined some existing load balancing

algorithm that maintain load balancing and provide

better scheduling and resource allocation techniquesResearchers have been done in this area. But still there

is need of improvement in the strategy of resource

allocation and scheduling algorithm.

REFERENCES:

[1] Gaochao Xu, Junjie Pang, and Xiaodong Fu A Load

Balancing Model Based on Cloud Partitioning for the

Public Cloud. IEEE TRANSACTIONS ON CLOUD

COMPUTING YEAR 2013

[2] R. Hunter, The why of cloud

http://www.gartner.com/ DisplayDocument?doc

cd=226469&ref= g noreg, 2012

[3]Namrata Swarnkar , Asst. Prof. Atesh Kumar Singh

and Dr. R. Shankar A Survey of Load Balancing

Techniques in Cloud Computing. Vol. 2Issue 8, August2013

[4] Rajkumar Buyyaa and et.al. Cloud computing and

emerging IT platforms: Vision, hype, and reality for

delivering computing as the 5th utility ,ELSEVIER.

[5] NIST: Nist definition of cloud computing

[6] Fox et al: Above the Clouds: A Berkeley View of

Cloud computing feb 2009 [7] cloud computing bible.

[8] Rajwinder Kaur and Pawan Luthra Load

Balancing in Cloud Computing. Proc. of Int. Conf. on

Recent Trends in Information, Telecommunication and

Computing, ITC

[9] Y. Fang, F. Wang, and J. Ge, A Task SchedulingAlgorithm Based on Load Balancing in Cloud

Computing, Web Information Systems and Mining,

Lecture Notes in Computer Science, Vol. 6318, 2010pages 271-277.

[10] Ms. Parin. V. Patel, Mr. Hitesh. D. Patel and Asst

Prof. Pinal. J. Patel A Survey On Load Balancing In


6/6

www.ijsret.org



Cloud Computing. International Journal of Engineering

Research & Technology (IJERT)Vol.

1 Issue 9, November-2012

[11] Nishant, K. P. Sharma, V. Krishna, C. Gupta, KP.

Singh, N. Nitin and R. Rastogi, Load Balancing of Nodes

in Cloud Using Ant Colony Optimization. In proc. 14th

International Conference on Computer Modelling and

Simulation (UKSim), IEEE, pp: 3-8, March 2012.[12] Dhinesh Babu L.D, P. VenkataKrishna, Honey bee

behavior inspired load balancing of tasks in cloud

computing environments,Applied Soft Computing

13 (2013)

[13]Dongliang Zhang, Changjun Jiang,Shu Li, A fast

adaptive load balancing method for parallel particle-based

simulations.

[14] T.R.V. Anandharajan, Dr. M.A. BhagyaveniCo-

operative Scheduled Energy Aware Load- Balancingtechnique for an Efficient Computational Cloud IJCSI

International Journal of Computer Science Issues, Vol. 8,

Issue 2, March 2011[15] Yunhua Deng, Rynson W.H. Lau, Heat diffusion

based dynamic load balancing for distributed virtualenvironments. in: Proceedings of the17th ACM

Symposium on Virtual Reality Software and Technology,

ACM, 2010, pp. 203210

[16] P.Warstein, H.Situ and Z.Huang(2010), Load

balancing in a cluster computer In proceeding of theseventh International Conference on Parallel and

Distributed Computing, Applications and Technologies,

IEEE.

[17] Ms.NITIKA, Ms.SHAVETA, Mr. GAURAV RAJ;

Comparative Analysis of Load Balancing Algorithms inCloud Computing, International Journal of Advanced

Research in Computer Engineering & Technology

Volume 1, Issue 3, May 2012.

[18] A. Paulin Florence and V. Shanthi A LOAD

BALANCING MODEL USING FIREFLY ALGORITHM

IN CLOUD COMPUTING. 2014

Science Publications

[19] R.R. Kotkondawar, P.A. Khaire, M.C. Akewar and

Y.N. Patil, A Study of Effective Load Balancing

Approaches in Cloud Computing.International Journal of

Computer Applications , Volume 87 No.8, February

2014[20] Rajesh George Rajan and V.Jeyakrishnan A Survey

on Load Balancing in Cloud Computing Environments.

International Journal of Advanced Research in Computer

and Communication Engineering Vol. 2, Issue 12,

December 2013

[21] SHANTI SWAROOP MOHARANA1,

RAJADEEPAN D. RAMESH and DIGAMBER

POWAR ANALYSIS OF LOAD BALANCERS IN

CLOUD COMPUTING. Vol. 2, Issue 2, May 2013,

101-108 , IASET

[22] Uddalak Chatterjee A Study on Efficient Load

Balancing Algorithms in Cloud Computing Environment

International Journal of Current Engineering and

Technology ,2013[23] Nidhi Jain Kansal and Inderveer Chana Cloud Load

Balancing Techniques : A Step Towards Green

Computing. IJCSI International Journal of Computer

Science Issues, Vol. 9, Issue 1, No 1, January 2012

[24] Tushar Desai and Jignesh Prajapati A Survey Of

Various Load Balancing Techniques And Challenges In

Cloud Computing. INTERNATIONAL JOURNAL OF

SCIENTIFIC & TECHNOLOGY RESEARCH

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Different Strategies for Load Balancing in Cloud Computing Environment: a critical Study