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Reseach Article

Load Balancing in Distributed System Using Genetic Algorithm

Published on October 2011 by Purnima Shah, S. M. Shah
IP Multimedia Communications
Foundation of Computer Science USA
IPMC - Number 1
October 2011
Authors: Purnima Shah, S. M. Shah
0fc62cf8-35d9-42a9-af04-a11241066789

Purnima Shah, S. M. Shah . Load Balancing in Distributed System Using Genetic Algorithm. IP Multimedia Communications. IPMC, 1 (October 2011), 139-142.

@article{
author = { Purnima Shah, S. M. Shah },
title = { Load Balancing in Distributed System Using Genetic Algorithm },
journal = { IP Multimedia Communications },
issue_date = { October 2011 },
volume = { IPMC },
number = { 1 },
month = { October },
year = { 2011 },
issn = 0975-8887,
pages = { 139-142 },
numpages = 4,
url = { /specialissues/ipmc/number1/3767-ipmc031/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 IP Multimedia Communications
%A Purnima Shah
%A S. M. Shah
%T Load Balancing in Distributed System Using Genetic Algorithm
%J IP Multimedia Communications
%@ 0975-8887
%V IPMC
%N 1
%P 139-142
%D 2011
%I International Journal of Computer Applications
Abstract

Distributed systems are characterized by resource multiplicity and system transparency. A variety of widely differing techniques and methodologies for scheduling processes of a distributed system have been proposed. These techniques are broadly classified into three types: task allocation approach, load balancing, load sharing. The main goal of load balancing is to equalize the workload among the nodes by minimizing execution time, minimizing communication delays, maximizing resource utilization and maximizing throughput. The scheduling in distributed system is NP-complete problem even in best conditions, and methods based on heuristic search have been proposed to obtain optimal and suboptimal solutions. This paper presents a new concept for process scheduling in distributed system considering load balancing. In this paper, using the power of genetic algorithms we have shown how to perform load balancing efficiently.

References
  1. M. Deriche, M.K. Huang, and Q.T. Tsai, “Dynamic Load-Balancing in Distributed Heterogeneous Systems under Stationary and Bursty Traffics,” Proc. 32nd Midwest Symp. Circuits and Systems, vol. 1, pp. 669-672, 1990.
  2. D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, Mass.: Addison-Wesley, 1989.M.
  3. Sandeep Sharma, Sarabjit Singh, and Meenakshi Sharma “Performance Analysis of Load Balancing Algorithms” World Academy of Science, Engineering and Technology 38 2008.
  4. Melanie Mitchell, An introduction to genetic algorithms: Easterm Economy Edition, 2004.
  5. C. Lu and S.-M. Lau. A performance study on load balancing algorithms with process migration. In Proceedings, IEEE TENCON 1994, pages 357-64, Aug.1994.
  6. Y. T. Wang and R. J. T. Morris Load sharing in distributed systems. IEEE Trans. Comput., C-34(3), Mar. 1985.
  7. Gilbert Syswerda, Jeff Palmucci, "The application of Genetic Algorithms to Resource Scheduling," Proc. Fourth International Conference on Genetic Algorithms, pp.502-508, 1991.
Index Terms

Computer Science
Information Sciences

Keywords

Distributed systems genetic algorithm load balancing scheduling