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Comparative Study of Load Balancing Algorithms in Cloud Computing Environment


  • Faculty of Computing Science and Engineering, VIT University, Vellore - 632014, Tamil Nadu, India
  • PKIET, Karaikal - 609603, Puducherry, India


Background: Cloud computing is an emerging technology in a business. It is used to access the application or services and infrastructures at anywhere any time. Load balance means that to share the work across the multiple computing resources to serve higher for the user and utilize the resource with efficiency for reach the good performance of the application. These ideas are enforced with software system, hardware or each. Statistical Analysis: The various load balancing algorithms are compared with quality of service parameters in a cloud network. This analysis helps to identify the effective load balancing algorithm for optimizes resource use, maximizes throughput, minimizes response time, and avoids overload. Findings: The load balancer is indorsed in all situations for to supply service continuity and handling additional traffic. Therefore the effective load balancing algorithms needed to form economical resource utilization by provisioning of resources to cloud user's on-demand basis. Application: This paper discusses numerous load balancing algorithms so as to improve resource utilization and quality of services in cloud computing environment.


Cloud Computing Model, Cloud Computing Characteristics, Load Balancing, Task Scheduling, Virtual Machine.

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  • Hurwitz J, Bloor R, Kaufman M, Halper F. Cloud computing for dummies. John Wiley and Sons; 2010 Jan 19.
  • Rajkumar B, Yeo CS, Venugopal S, Broberg J, Brandic I. Cloud computing and emerging IT platforms. Future Generation Computer Systems. Elsevier Press, Inc.; 2009.
  • Hardware, software load balancing in cloud environment, Macromedia Press; 2003. Available from: http://www.adobepress. com/articles/article.asp?p=31089&seqNum=5. 28/02/2003
  • Singh A, Juneja D, Malhotra M. Autonomous agent based load balancing algorithm in cloud computing. Procedia Computer Science. 2015 Dec 31; 45:832-41.
  • Liu G, Li J, Xu J. An improved min-min algorithm in cloud computing. Proceedings of the 2012 International Conference of Modern Computer Science and Applications; Berlin Heidelberg: Springer. 2013. p. 47-52.
  • Kokilavani T, Amalarethinam DD. Load balanced min-min algorithm for static meta-task scheduling in grid computing. International Journal of Computer Applications. 2011 Apr; 20(2):43-9.
  • Bhoi U, Ramanuj PN. Enhanced max-min task scheduling algorithm in cloud computing. International Journal of Application or Innovation in Engineering and Management. 2013 Apr; 2(4):259-64.
  • Balaji N, Umamakeshwari A. Load balancing in virtualized environment - A survey. Indian Journal of Science and Technology. 2015 May 1; 8(S9):230-4.
  • Samal P, Mishra P. Analysis of variants in Round Robin Algorithms for load balancing in Cloud Computing. IJCSIT. 2013; 4(3):416-9.
  • James J, Verma B. Efficient VM load balancing algorithm for a cloud computing environment. International Journal on Computer Science and Engineering. 2012 Sep 1; 4(9):1658-63.
  • Dasgupta K, Mandal B, Dutta P, Mandal JK, Dam S. A Genetic Algorithm (GA) based load balancing strategy for cloud computing. Procedia Technology. 2013 Dec 31; 10:340-7.
  • LD DB, Krishna PV. Honey bee behavior inspired load balancing of tasks in cloud computing environments. Applied Soft Computing. 2013 May 31; 13(5):2292-303.
  • Anju Baby J. A survey on honey bee inspired load balancing of tasks in cloud computing. International Journal of Engineering Research and Technology. 2013 Dec 18; 2(12):1442-5.
  • Nakrani S, Tovey C. On honey bees and dynamic server allocation in internet hosting centers. Adaptive Behavior. 2004 Dec 1; 12(3-4):223-40.
  • Chakaravarthy T, Kalyani K. A brief survey of honey bee mating optimization algorithm to efficient data clustering. Indian Journal of Science and Technology. 2015 Sep 16; 8(24):1-7.
  • Mishra R, Jaiswal A. Ant colony optimization: A solution of load balancing in cloud. International Journal of Web and Semantic Technology. 2012 Apr 1; 3(2):33-50.
  • Li K, Xu G, Zhao G, Dong Y, Wang D. Cloud task scheduling based on load balancing ant colony optimization. 6th Annual IEEE Chinagrid Conference (China Grid); 2011 Aug 22. p. 3-9.
  • Sakthipriya N, Kalaipriyan T. Variants of ant colony optimization- a state of an art. Indian Journal of Science and Technology. 2015 Nov 14; 8(31):1-15.
  • Hung CL, Wang HH, Hu YC. Efficient load balancing algorithm for cloud computing network. International Conference on Information Science and Technology (IST 2012); 2012 Apr 28. p. 28-30.
  • Xu G, Pang J, Fu X. A load balancing model based on cloud partitioning for the public cloud. Tsinghua Science and Technology. 2013 Feb; 18(1):34-9.
  • Mondal B, Dasgupta K, Dutta P. Load balancing in cloud computing using stochastic hill climbing - A soft computing approach. Procedia Technology. 2012 Dec 31; 4:783-9.
  • Florence AP, Shanthi V. A load balancing model using firefly algorithm in cloud computing. Journal of Computer Science. 2014 Jul 1; 10(7):1156-65.
  • Vardhini KK, Sitamahalakshmi T. A Review on nature- based Swarm intelligence optimization techniques and its current research directions. Indian Journal of Science and Technology. 2016 Mar 16; 9(10):1-13.


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