Fuzzy Logic–Based Scheme for Load Balancing in Grid Services


Load balancing is essential for efficient utilization of resources and enhancing the responsiveness of a computational grid, especially that hosts of services most frequently used, i.e. food, health and nutrition. Various techniques have been developed and applied; each has its own limitations due to the dynamic nature of the grid. Efficient load balancing can be achieved by an effective measure of the node’s/cluster’s utilization. In this paper, as a part of an NSTIP project # 10-INF1381-04 and in order to assess of FAQIH framework ability to support the load balance in a computational grid that hosts of food, health and nutrition inquire services. We detail the design and implementation of a proposed fuzzy-logic-based scheme for dynamic load balancing in grid computing services. The proposed scheme works by using a fuzzy logic inference system which uses some metrics to capture the variability of loads and specifies the state of each node per a cluster. Then, based on the overall nodes’ states, the state of the corresponding cluster will be defined in order to assign the newly arrived inquires such that load balancing among different clusters and nodes is accomplished. Many experiments are conducted to investigate the effectiveness of the proposed fuzzy-logic-based scheme to support the load balance where the results show that the proposed scheme achieves really satisfactory and consistently load balance than of other randomize approaches in grid computing services.

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T. Helmy, H. Al-Jamimi, B. Ahmed and H. Loqman, "Fuzzy Logic–Based Scheme for Load Balancing in Grid Services," Journal of Software Engineering and Applications, Vol. 5 No. 12B, 2012, pp. 149-156. doi: 10.4236/jsea.2012.512B029.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] C. Kesselman, S. Tuecke, “The Anatomy of the Grid: Enabling Scalable Virtual Organizations,” International Journal of High Performance Computing Applications, vol. 15, issue3, pp 200–222, 2001.
[2] S. Sharma, S. Singh, and M. Sharma, "Performance Analysis of Load Balancing Algorithms," World Academy of Science, Engineering and Technology, vol. 38, 2008.
[3] L. A. Zadeh, “Fuzzy Logic, Neural Networks, and Soft Com-puting, Communications of the ACM, vol-37, Issue-3, pp: 77-84, Mar. 1994.
[4] L. A. Zadeh, “From Computing with numbers to computing with words-from manipulation of measurements to manipulation of perceptions”, Int. J. Appl. Math. Computer Sci., Vol.12, No. 3, pp. 307-324, 2002.
[5] H. T. Nguyen, N. R. Prasad, C. L. Walker, and E. A. Walker, “A First Course in Fuzzy and Neural Control”, CRC Press, 2002.
[6] B. Kosko, Neural Networks and F u q Systems: A Dynamical Systems Approach to Machine Intelligence, Prentice Hall, New Jersey, 1992.
[7] L. S. Cheung. Load Balancing in Distributed Object Computing Systems, M. Phil. Thesis, Department of Electrical and Electronic Engineering, the University of Hong Kong, August 2001
[8] S. R. Moosavi-Nejad, S.S. Mortazavi, and B. V. Vahdat, “Fuzzy based Design and tuning of distributed systems load balancing controller,” The 5th Symposium on Advances in Science and Technology (SASTech), 2011.
[9] K. B Bey, F. Benhammadi, Z. Gessoum and A. Mokhtari, "CPU Load Prediction Using Neuro-Fuzzy and Bayesian Inferences", Journal of Neurocomputing Vol.74, pp. 1606-1616, May. 2011.
[10] A. Revar, M. Andhariya, D. Sutariya, "Load Balancing in Grid Environment using Machine Learning - Innovative Approach, " International Journal of Computer Applications (0975 – 8887), Volume 8– No.10, October 2010
[11] K. V. Yu, and Chih-Hsun Chou, “A Fuzzy-Based Dynamic Load-Balancing Algorithm,” http://jitas.im.cpu.edu.tw/2004-2/4.pdf
[12] M. Rantonen, Tapio Frantti, and Kauko Leivisk, "Fuzzy expert system for load balancing in symmetric multipro-cessor systems ", Expert Systems with Applications Journal, Vol. 37, Issue 12, pp. 8711-8720, December 2010
[13] L. Cheung and Y. Kwok, “On load balancing approaches for distributed object computing systems,” The Journal of Supercomputing, vol. 27, pp. 149-175, 2004.
[14] E. Saravanakumar and Gomathy Prathima,” A novel load balancing algorithm for computational grid, “International Journal of Computational Intelligence Techniques, ISSN: 0976–0466 & E-ISSN: 0976–0474 Volume 1, Issue 1, 2010, PP-20-26
[15] K. Lu, R. Subrata, and A.Y. Zomaya, “An Efficient Load Balancing Algorithm for Heterogeneous Grid Systems Considering Desirability of Grid Sites,” Proc. 25th IEEE Int’l Performance Computing and Comm. Conf. (IPCCC ’06), 2006.
[16] Y. Li, Yuhang Yang and Rongbo Zhu, “A Hybrid Load balancing Strategy of Sequential Tasks for Computational Grids,” 2009 IEEE International Conference on Networking and Digital Society, pp.112-117.
[17] B. Yagoubi and Y. Slimani, “Task Load Balancing Strategy for Grid Computing,” Journal of Computer Science 3 (3): 186-194, 2007.
[18] L. M. Khanli1 and Behnaz Didevar, “A New Hybrid Load Balancing Algorithm in Grid Computing Systems, ”Journal of Computer Science Vol-2 No 5 October, 2011.
[19] D. Ramesh and A. Krishnan, “Hybrid Algorithm for Optimal Load Sharing in Grid Computing, Journal of Computer Science 8 (1): 175-180, 2012.
[20] T. Helmy, F. Al-Otaibi, “Dynamic Load-Balancing Based on a Coordinator and Backup Automatic Election in Distributed Systems“, International Journal of Computing & Information Science, pp. 37-45, Vol. 9, No. 1, April 2011.

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