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Dynamic task scheduling using genetic algorithms for heterogeneous distributed computing

Bibtex

@inproceedings{ PKN2004c,
title= {Dynamic task scheduling using genetic algorithms for heterogeneous distributed computing},
booktitle={Proceedings of the 19th IEEE/ACM International Parallel and Distributed Processing Symposium},
author= {Andrew J. Page and Thomas J. Naughton},
month= {April},
year = {2005},
address = {Denver, Colorado, USA},
publisher={IEEE Computer Society}
}

Abstract

An algorithm has been developed to dynamically schedule heterogeneous tasks on heterogeneous processors in a distributed system. The scheduling strategy operates in an environment with dynamically changing resources and adapts to variable communication costs and variable availability of processing resources. The scheduler operates in a batch fashion and utilises a genetic algorithm to minimise the overall execution time. We have compared our scheduler to six other schedulers, three batch-mode schedulers and three immediate-mode schedulers. We have performed simulations with randomly generated task sets, using uniform, normal, and Poisson distributions, whilst varying the communication overheads between the clients and scheduler. We have achieved more efficient schedulers then all other schedulers across a range of different scenarios while scheduling 10,000 tasks on up to 50 heterogeneous processors.

Keywords

  • java
  • adaptive scheduling
  • open source
  • batch scheduling
  • distributed computing
  • dynamic scheduling
  • genetic algorithms

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