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Enhancing Load Balancing in Cloud Computing by Ant Colony Optimization Method

Prachi Verma, Sonika Shrivastava, R.K. Pateriya

Department of Computer Science & Engineering, MANIT, Bhopal, 462003, India
:10.22362/ijcert/2017/v4/i6/xxxx [UNDER PROCESS]

Cloud computing is an evolving technology which provides users “pay as you go” services on demand. Nowadays there is a tremendous increase in the use of the cloud by the clients due to its attractive features which results in a rapid growth of load on servers. Hence, load balancing has become a matter of concern in the domain of cloud computing. Load balancing is required to distribute the workload equally amongst all nodes in a network so that none of a node is overloaded or underloaded and each node does a similar amount of work in equal time. It minimizes the cost and time involved in the major computational models and helps to improve proper utilization of resources and system performance. Many approaches and algorithms are recommended by various researchers from all over the world to solve the problem of load balancing. In this paper, we present a technique built on Ant Colony optimization to address the issue of load balancing in a cloud environment.

Prachi Verma, Sonika Shrivastava, & R.K. Pateriya. (2017). Enhancing Load Balancing in Cloud Computing by Ant Colony Optimization Method. International Journal of Computer Engineering In Research Trends, 4(6), 269-276. Retrieved from

Keywords : Cloud Computing; Ant colony optimization, Swarm intelligence; Load Balancing;

1 D. Saranya, "Load Balancing Algorithms in Cloud Computing: A Review," International Journal of Advanced Research in Computer Science and Software Engineering, vol. 5, Issue 7, July 2015.
2 S. Sethi, "Efficient Load Balancing in Cloud Computing using Fuzzy Logic," IOSR Journal of Engineering (IOSRJEN) ISSN: 2250-3021 vol. 2, pp. 65-71, July 2012.
3 T. Desai, "A Survey of Various Load Balancing Techniques and Challenges in Cloud Computing," International Journal of Scientific & Technology Research, vol. 2, Issue 11, November 2013.
4 S. Rajoriya, "Load Balancing Techniques in Cloud Computing: An Overview," International Journal of Science and Research (IJSR), vol. 3, Issue 7, July 2014
5 Sharma S., “Performance Analysis of Load Balancing Algorithms,” World Academy of Science, Engineering and Technology, 38, 2008.
6 Gross D., “Noncooperative load balancing in distributed systems”, Elsevier, Journal of Parallel and Distributed Computing, No. 65, pp. 1022-1034, 2005.
7 Nikravan M., “A Genetic Algorithm for Process Scheduling in Distributed Operating Systems Considering Load Balancing”, Proceedings 21st European Conference on Modelling and Simulation (ECMS), 2007.
8 M. Amar, “SLA Driven Load Balancing for Web Applications in Cloud Computing Environment”, Information and Knowledge Management, 1(1), pp. 5-13, 2011.
9 Ekta Gupta, “A Technique Based on Ant Colony Optimization for Load Balancing in Cloud Data Center”, 13th International Conference on Information Technology, 2014 IEEE.
10 M. Katyal, “A Comparative Study of Load Balancing Algorithms in Cloud Computing Environment”, International Journal of Distributed and Cloud Computing Volume 1 Issue 2 December 2013 
11 S. Khan, “Effective Scheduling Algorithm for Load Balancing using Ant Colony Optimization in Cloud Computing”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 2, February 2014..
12 Kun Li, “Cloud Task scheduling based on Load Balancing Ant Colony Optimization”, 2011 Sixth Annual ChinaGrid Conference, 2011 IEEE.
13 D. Kashyap, “A Survey Of Various Load BalancingAlgorithms In Cloud Computing”, INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 3, ISSUE 11, NOVEMBER 2014. 
14 Book: Ant colony optimization by Macro Dorigo and Thomas Stutzle.
15 R. Rastogi, "Load Balancing of Nodes in Cloud Using Ant Colony Optimization." Proceedings of the 14th International Conference on Computer Modelling and Simulation (UKSim), March 2012, IEEE, pp: 3-8.
16 Calheiros, R.N.; Ranjan, R.; Beloglazov, A.; de Rose, C.A.F.; Buyya, R. CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software 2011, 41, 23–50.
17. T.Deepa, & S Sharon Amulya Joshi. (2016). A Survey on Load Balancing Algorithms in Cloud.
International Journal of Computer Engineering In Research Trends, 3(7), 371-374. Retrieved from

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