Title :
A stochastic genetic algorithm for dynamic load balancing in distributed systems
Author :
Munetomo, Masaharu ; Takai, Yoshiaki ; Sato, Yoshiharu
Author_Institution :
Dept. of Inf. & Data Anal., Hokkaido Univ., Sapporo, Japan
Abstract :
This paper presents a genetic algorithm (GA) for stochastic environments and its application to dynamic load balancing in distributed systems. We have proposed a stochastic genetic algorithm (StGA) which has an evaluation mechanism for fitness values based on the reinforcement learning in order to adapt to stochastic environments. We apply the StGA to the decision phase of task migration requests in dynamic load balancing, and we realize a task distribution system based on the StGA in a local area network which consists of UNIX workstations
Keywords :
distributed processing; genetic algorithms; learning (artificial intelligence); resource allocation; stochastic processes; LAN; UNIX workstations; distributed systems; dynamic load balancing; evaluation mechanism; fitness values; local area network; reinforcement learning; stochastic genetic algorithm; task distribution system; task migration requests; Convergence; Data analysis; Genetic algorithms; Learning automata; Load management; Local area networks; Stochastic processes; Stochastic systems; Systems engineering and theory; Workstations;
Conference_Titel :
Systems, Man and Cybernetics, 1995. Intelligent Systems for the 21st Century., IEEE International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-2559-1
DOI :
10.1109/ICSMC.1995.538379
