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Reseach Article

Performance Analysis of Load Balancing Algorithms in Cloud Computing

by Rajeev Kumar, Tanya Prashar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 120 - Number 7
Year of Publication: 2015
Authors: Rajeev Kumar, Tanya Prashar
10.5120/21240-4016

Rajeev Kumar, Tanya Prashar . Performance Analysis of Load Balancing Algorithms in Cloud Computing. International Journal of Computer Applications. 120, 7 ( June 2015), 19-27. DOI=10.5120/21240-4016

@article{ 10.5120/21240-4016,
author = { Rajeev Kumar, Tanya Prashar },
title = { Performance Analysis of Load Balancing Algorithms in Cloud Computing },
journal = { International Journal of Computer Applications },
issue_date = { June 2015 },
volume = { 120 },
number = { 7 },
month = { June },
year = { 2015 },
issn = { 0975-8887 },
pages = { 19-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume120/number7/21240-4016/ },
doi = { 10.5120/21240-4016 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:05:36.985942+05:30
%A Rajeev Kumar
%A Tanya Prashar
%T Performance Analysis of Load Balancing Algorithms in Cloud Computing
%J International Journal of Computer Applications
%@ 0975-8887
%V 120
%N 7
%P 19-27
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Cloud computing is a business oriented IT-technology, which is composed of multiple computing technologies accessed via internet. With the rapid increase in cloud usage, it becomes a challenge to deliver the cloud services effectively and efficiently to the cloud consumers on the pay-per usage basis. In this concern Balancing of load has become one of the essential components for the cloud computing environment to perform the effective operations. Scheduling of virtual machines or data centers has to be done properly by using an appropriate load balancing technique. Hence, several algorithms have been developed to process the client's request towards the cloud nodes. . In this present work, a hybridized swarm intelligence technique is proposed to evenly distribute the incoming task requests among the virtual machines or server. Additionally, the performance analysis has been performed using the CloudAnalyst simulator. This paper gives a comprehensive performance analysis of the proposed approach and compares its results with existing Round Robin (RR), Equally Spread Current Execution (ESCE) and ant colony optimization (ACO) techniques. Simulation results have demonstrated that the proposed technique shows a significant outcome in terms of response time, data center processing time and total cost in cloud computing.

References
  1. Sowmya Suryadevera, Jaishri Chourasia, Sonam Rathore, Abdul Jhummarwala. (2012). Load Balancing in Computational Grids Using Ant Colony Optimization Algorithm. International Journal of Computer & Communication Technology (IJCCT).
  2. Kumar Nishant, Pratik Sharma, Vishal Krishna, Chhavi Gupta and Kunwar Pratap Singh, Nitin and Ravi Rastogi. 2012 Load Balancing of Nodes in Cloud Using Ant Colony Optimization. 14th International Conference on Modelling and Simulation.
  3. Kuppani Sathish , A Rama Mohan Reddy, ( Octobe 2008) Enhanced ant algorithm based load balanced task scheduling in grid computing, IJCSNS International Journal of Computer Science and Network Security, VOL. 8 No. 10, 21910.
  4. Ratan Mishra and Anant Jaiswal, April 2012 Ant colony Optimization: A Solution of Load balancing in Cloud, International Journal of Web & Semantic Technology (IJWesT) Vol. 3, No. 2.
  5. A. Y. Zomaya, & Y. H. Teh. (2001). Observations on using genetic algorithms for dynamic load-balancing. IEEE Transaction on Parallel and Distributed Systems, vol. 12, no. 9, pp. 899-911.
  6. Shanti Swaroop Moharana, Rajadeepan D. Ramesh & Digamber Powar. (May 2013) Analysis of Load Balancers in Cloud Computing, In International Journal of Computer Science and Engineering (IJCSE), ISSN 2278-9960 Vol. 2, Issue 2, 101-108 ©IASE.
  7. M. Houle, A. Symnovis and D. Wood, (June 2002). Dimension-exchange algorithms for load balancing on trees, in: Proc. of 9th Int. Colloquium on Structural Information and Communication Complexity, Andros, Greece, pp. 181–196.
  8. Y. Hu, R. Blake and D. Emerson, (1998). An Optimal Migration Algorithm for Dynamic Load Balancing, Concurrency: Practice and Experience 10 , pp 467–483.
  9. Daniel Grosu, Anthony T. Chronopoulos et. al. (2005) 'Noncooperative load balancing in distributed systems', Journal of Parallel and Distributed Computing, Elsevier, Volume 65, Issue 9, pp 1022 – 1034.
  10. M. Salehi & H. Deldari. (2006) Grid Load Balancing using an Echo System of Intelligent Ants, Proceedings of the 24th IASTED International Conference on Parallel and Distributed Computing and Networks, pp. 47-52.
  11. Pandey S, Wu L, Guru S, Buyya R. (2010) A particle swarm optimization-based heuristic for scheduling workflow applications in Cloud Computing environments, In: International conference on advanced information networking and applications (AINA), IEEE Computer Society; p. 400–7.
  12. Dhinesh Babu L. D. and P. Venkata Krishna, 2013 Honey bee behaviour inspired load balancing of tasks in cloud computing environments, Applied Soft Computing, Vol. 13, No. 5, pp. 2292–2303.
  13. Mr. Manan D. Shah, February 2013 Allocation of Virtual Machines In Cloud Computing Using Load Balancing Algorithm, in International Journal of Computer Science and Information Technology & Security (IJCSITS), ISSN: 2249-9555 Vol. 3, No. 1.
  14. Ms. Nitika, Ms. Shaveta, Mr. Gaurav Raj, May 2012 Comparative Analysis of Load Balancing Algorithms in Cloud Computing, in International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 3,
  15. Dorigo, M. , Gambardella, (1997). L. M. : Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 53–66.
  16. Salim Bitam, 2012 Bees Life Algorithm for Job Scheduling in Cloud Computing, Proceedings of The Third International Conference on Communications and Information Technology, pp. 186-191.
  17. Rajkumar Buyya, Chee Shin Yeo, Srikumar Venugopal, James Broberg, and Ivona Brandic. (June 2009). Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility, Future Generation Computer Systems, Volume 25, And Number 6, ISSN: 0167-739X, Elsevier Science, Amstersdam, The Netherlands, Pages: 599-616.
Index Terms

Computer Science
Information Sciences

Keywords

Load Balancing Cloud Computing Priority based Bee Colony ACO.