Page, Andrew J. and Naughton, Thomas J. (2005) Framework for task scheduling in heterogeneous distributed computing using genetic algorithms. Artificial Intelligence Review, 24 (3-4). pp. 415-429.

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Abstract

An algorithm has been developed to dynamically schedule heterogeneous tasks on heterogeneous processors in a distributed system. The scheduler operates in an environment with dynamically changing resources and adapts to variable system resources. It operates in a batch fashion and utilises a genetic algorithm to minimise the total execution time. We have compared our scheduler to six other schedulers, three batch-mode and three immediate-mode schedulers. Experiments show that the algorithm outperforms each of the others and can achieve near optimal efficiency, with up to 100,000 tasks being scheduled.

Item Type:	Article
Additional Information:	This journal paper is an extended version of a conference paper presented at AICS'04
Keywords:	distributed computing, genetic algorithms, task scheduling
Academic Unit:	Faculty of Science and Engineering > Computer Science
Item ID:	1035
Depositing User:	Andrew Page
Date Deposited:	31 Jul 2008
Journal or Publication Title:	Artificial Intelligence Review
Publisher:	Springer
Refereed:	Yes
URI:
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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