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Framework for task scheduling in heterogeneous distributed computing using genetic algorithms

Bibtex

@inproceedings{ PAN2004c,
title = {Framework for task scheduling in heterogeneous distributed computing using genetic algorithms},
author = {Andrew J. Page and Thomas J. Naughton},
booktitle= {15th Artificial Intelligence and Cognitive Science Conference},
editors = {Lorraine McGinty and Brian Crean},
isbn = {1-902277-89-9},
month = {September},
year = {2004},
address = {Castlebar, Ireland},
pages = {137--146}
}

Abstract

An algorithm has been developed to dynamically schedule heterogeneous tasks on to heterogeneous processors in a distributed system. The scheduling strategy operates in a dynamically changing computing resource environment and adapts to variable communication costs and variable availability of processing resources. The scheduler utilises a genetic algorithm to minimise the overall execution time. Experiments are performed which show that the algorithm can achieve near optimal efficiency, with up to 100,000 tasks being scheduled.

Keywords

  • genetic algorithms
  • java
  • adaptive scheduling
  • dynamic scheduling
  • task scheduling
  • distributed computing
  • heterogeneous computing

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