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A Load Sharing Algorithm Including An Improved Response Time using Evolutionary Information in Distributed Systems

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초록

A load sharing algorithm is one of the important factors 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 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.

Abstract

A load sharing algorithm is one of the important factors 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 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.

참고문헌 (13)

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  8. N.G.Shivaratri, "Two Adaptive Location Policies for Global Scheduling Algorithms", Proc. 10th International Conference on Distributed Computing Systems, pp.502~509 (1990)
  9. Terence C. Fogarty, "Use of the Genetic Algorithm for Load Balancing of Sugar Beet Presses", Proc. Sixth International Conference on Genetic Algorithms, pp.617~624 (1995)
  10. Garrism W. Greenwood, "Scheduling Tasks in Real-Time Systems using Evolutionary Strategies", Third Workshop on Parallel and Distributed Real-Time Systems, pp.195~196 (1995)
  11. Gilbert Syswerda, "The application of Genetic Algorithms to Resource Scheduling", Proc. Fourth International Conference on Genetic Algorithms, pp.502~508 (1991)
  12. Mitchell, "An Introduction to Genetic Algorithms" (1996), MIT Press
  13. M.Srinivas, "Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms", IEEE Trans on SMC, 24(4), pp.656~667 (1994)

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