Abstract
A load sharing mechanism is an important factor in computer system. In sender-initiated load sharing algorithms, when a distributed system becomes to heavy system load, it is difficult to find a suitable receiver because most processors have additional tasks to send. The sender continues to send unnecessary request messages for load transfer until a receiver is found while the system load is heavy. Because of these unnecessary request messages it results in inefficient communications, low cpu utilization, and low system throughput. To solve these problems, we propose a self-adjusting evolutionary algorithm approach for improved sender-initiated load sharing in distributed systems. This algorithm decreases response time and increases acceptance rate. Compared with the conventional sender-initiated load sharing algorithms, we show that the proposed algorithm performs better.
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References
Eager, D.L., Lazowska, E.D., Zahorjan, J.: Adaptive Load Sharing in Homogeneous Distributed Systems. IEEE Trans. on Software Engineering 12(5), 662–675 (1986)
Shivaratri, N.G., Krueger, P., Singhal, M.: Load Distributing for Locally Distributed Systems. IEEE COMPUTER 25(12), 33–44 (1992)
Grefenstette, J.: Optimization of Control Parameters for Genetic Algorithms. IEEE Trans. on SMC 16(1), 122–128 (1986)
Filho, J.R., Treleaven, P.C.: Genetic-Algorithm Programming Environments. IEEE COMPUTER, 28–43 (1994)
Kunz, T.: The Influence of Different Workload Descriptions on a Heuristic Load Balancing Scheme. IEEE Trans. on Software Engineering 17(7), 725–730 (1991)
Furuhashi, T., Nakaoka, K., Uchikawa, Y.: A New Approach to Genetic Based Machine Learning and an Efficient Finding of Fuzzy Rules. In: Furuhashi, T. (ed.) WWW 1994. LNCS, vol. 1011, pp. 114–122. Springer, Heidelberg (1995)
Miller, J.A., Potter, W.D., Gondham, R.V., Lapena, C.N.: An Evaluation of Local Improvement Operators for Genetic Algorithms. IEEE Trans. on SMC 23(5), 1340–1351 (1993)
Shivaratri, N.G., Krueger, P.: Two Adaptive Location Policies for Global Scheduling Algorithms. In: Proc. 10th International Conference on Distributed Computing Systems, May 1990, pp. 502–509 (1990)
Fogarty, T.C., Vavak, F., Cheng, P.: Use of the Genetic Algorithm for Load Balancing of Sugar Beet Presses. In: Proc. Sixth International Conference on Genetic Algorithms, pp. 617–624 (1995)
Greenwood, G.W., Lang, C., Hurley, S.: Scheduling Tasks in Real-Time Systems using Evolutionary Strategies. In: Proc. Third Workshop on Parallel and Distributed Real-Time Systems, pp. 195–196 (1995)
Syswerda, G., Palmucci, J.: The application of Genetic Algorithms to Resource Scheduling. In: Proc. Fourth International Conference on Genetic Algorithms, pp. 502–508 (1991)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)
Srinivas, M., Patnait, L.M.: Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms. IEEE Trans. on SMC 24(4), 656–667 (1994)
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Lee, S., Shim, D., Cho, D. (2006). A Self-adjusting Load Sharing Mechanism Including an Improved Response Time Using Evolutionary Information. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892960_14
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DOI: https://doi.org/10.1007/11892960_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46535-5
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