Title :
Automatic tuning of a fuzzy batch job scheduler using a genetic algorithm
Author :
Shaout, Adnan ; McAuliffe, Pattick
Author_Institution :
Dept. of Electr. & Comput. Eng., Michigan Univ., Dearborn, MI, USA
Abstract :
The paper presents the application of a genetic algorithm to automatically tune a fuzzy batch job scheduler for maximum throughput. This genetic algorithm varies fuzzy membership functions, fuzzy rules and resource limits on processors to optimize for maximum job throughput and load balancing across processors of a distributed system. Unlike most research done in the realm of load balancing and job scheduling, the paper presents an algorithm that has been evaluated in a production processing environment rather than in simulation only
Keywords :
batch processing (industrial); fuzzy set theory; genetic algorithms; knowledge based systems; processor scheduling; production control; resource allocation; scheduling; automatic tuning; distributed system; fuzzy batch job scheduler; fuzzy membership functions; fuzzy rules; genetic algorithm; job scheduling; load balancing; maximum job throughput; maximum throughput; production processing environment; resource limits; Biological cells; Engines; Fuzzy systems; Genetic algorithms; Genetic engineering; Hardware; Load management; Power system reliability; Processor scheduling; Throughput;
Conference_Titel :
Fuzzy Information Processing Society, 1999. NAFIPS. 18th International Conference of the North American
Conference_Location :
New York, NY
Print_ISBN :
0-7803-5211-4
DOI :
10.1109/NAFIPS.1999.781671
