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

fYear

1999

fDate

36342

Firstpage

144

Lastpage

148

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/NAFIPS.1999.781671

Filename

781671