Dynamic Load Redistribution Approach Using Genetic Information in Distributed Computing

Under sender-initiated load redistribution algorithms, 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 genetic algorithm approach for improved sender-initiated load redistribution in distributed systems, and define a suitable fitness function. This algorithm decreases response time and increases acceptance rate.

[1]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[2]  John A. Miller,et al.  An evaluation of local improvement operators for genetic algorithms , 1993, IEEE Trans. Syst. Man Cybern..

[3]  Garrison W. Greenwood,et al.  Scheduling tasks in real-time systems using evolutionary strategies , 1995, Proceedings of Third Workshop on Parallel and Distributed Real-Time Systems.

[4]  Anna Hác,et al.  Dynamic Load Balancing in a Distributed System Using a Sender-Initiated Algorithm , 1987, SIGMETRICS Perform. Evaluation Rev..

[5]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[6]  Thomas Kunz,et al.  The Influence of Different Workload Descriptions on a Heuristic Load Balancing Scheme , 1991, IEEE Trans. Software Eng..

[7]  Cesare Alippi,et al.  Genetic-algorithm programming environments , 1994, Computer.

[8]  Terence C. Fogarty,et al.  Use of the Genetic Algorithm for Load Balancing of Sugar Beet Presses , 1995, ICGA.

[9]  Gilbert Syswerda,et al.  The Application of Genetic Algorithms to Resource Scheduling , 1991, International Conference on Genetic Algorithms.

[10]  Phillip Krueger,et al.  Two adaptive location policies for global scheduling algorithms , 1990, Proceedings.,10th International Conference on Distributed Computing Systems.

[11]  Y. Uchikawa,et al.  A New Approach to Genetic Based Machine Learning and an Efficient Finding of Fuzzy Rules - Proposal of Nagoya Approach - , 1994, IEEE/Nagoya-University World Wisepersons Workshop.

[12]  Albert Y. Zomaya,et al.  Observations on Using Genetic Algorithms for Dynamic Load-Balancing , 2001, IEEE Trans. Parallel Distributed Syst..