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GA-based Adaptive Load Balancing Method in Distributed Systems

  • Lee, Seong-Hoon (Department of Computer Science, Chonan University) ;
  • Lee, Sang-Gu (Department of Computer Engineering, Hannam University)
  • Published : 2002.03.01

Abstract

In the sender-initiated load balancing algorithms, the sender continues to send an unnecessary request message fur load transfer until a receiver is found while the system load is heavy. Meanwhile, in the receiver-initiated load balancing algorithms, the receiver continues to send an unnecessary request message for load acquisition until a sender is found while the system load is light. These unnecessary request messages result in inefficient communications, low CPU utilization, and low system throughput in distributed systems. To solve these problems, in this paper, we propose a genetic algorithm based approach fur improved sender-initiated and receiver-initiated load balancing. The proposed algorithm is used for new adaptive load balancing approach. Compared with the conventional sender-initiated and receiver-initiated load balancing algorithms, the proposed algorithm decreases the response time and increases the acceptance rate.

Keywords

References

  1. D. L. Eager, E. D. Lazowska, J. Zahorjan, 'Adaptive LoadSharing in Homogeneous Distributed Systems,' IEEE Trans.on Software Engieering, vol. 12, no. 5, pp. 662-675, May 1986
  2. N. G. Shivaratri, P. Krueger, and M. Singhal, 'Load Distri-buting for Locally Distributed Systerns,' IEEE Computer, vol.25, no. 12, pp. 33-14, Dec. 1992
  3. J. Grefenstette, 'Optimization of Control Parameters for Genetic Algorithms,' IEEE Trans. on SMC, vol. SMC-16, no. 1, pp. 122-128, Jan. 1996
  4. J. R. Filho and P. C. Treleaven, 'Genetic-Algorithm Programming Environments,' IEEE Computer, pp. 28-43,Jun. 1994
  5. T. Kunz, 'The Influence of Different Workload Descriptionson a Heuristic Load Balancing Scheme,' IEEE Trans. on Software Engineering, vol. 17, No. 7, pp. 725-730, Jul. 1991 https://doi.org/10.1109/32.83908
  6. T. Furuhashi, K. Nakaoka and Y. Uchikawa, "A New Approach to Genetic Based Machine Learning and an Efficient Finding of Fuzzy Rules" Proc. WWW. 94, pp. 114-122, 1994.
  7. J A. Miller, W D. Potter, R V. Gondham and C. N. Lapena,'An Evaluation of Local Improvement Operators for Genetic Algorithms,' IEEE Trans. on SMC, vol. 23, no. 5, pp. 1340-1451, Sep. 1993
  8. N. G. Shivaratri and P. Krueger, 'Two Adaptive Location Policies for Global Scheduling Algorithms,' Proc. 10th Inter-national Conference on Distributed Computing Systems, pp.502-509, May 1990
  9. T. C. Fogarty, F. Vavak and P. Cheng, 'Use of the Genetic Algorithm for Load Balancing of Sugar Beet Presses,' Proc. Sixth International Conference on Genetic Algorithms, pp. 195-624, 1995
  10. G. W. Greenwood, C. Lang and S. Hurley, 'SchedulingTasks in Real-Time Systems using Evolutionary Strate-gies,' Proc. Third Workshop on Parallel and Distributed Real-Time Systems, pp. 195-196, 1995
  11. M. Srinivas and L. M. Patnait, 'Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms,' IEEE Trans. on SMC, vol. 24, no. 4, pp. 656-667, Apr. 1994