Parameter setting and exploration of TAGS using a genetic algorithm

We consider the performance of TAGS, a multi-host job assignment policy. We use a genetic algorithm to compute the optimal parameter settings for the policy. We then explore the performance of the policy using the optimal parameters, when the job size distribution is a heavy-tailed bounded Pareto di...

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Bibliographic Details
Published in2007 IEEE Symposium on Computational Intelligence in Scheduling pp. 279 - 285
Main Authors Sarfati, H., Bachmat, E., Kedem-Yemini, S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2007
Subjects
Algorithm design and analysis
Computational intelligence
Computer science
Delay
Genetic algorithm
Genetic algorithms
Heavy-tailed distributions
Industrial engineering
Job shop scheduling
Multiple host task assignment
Processor scheduling
Technical Activities Guide -TAG
Web server
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Summary:We consider the performance of TAGS, a multi-host job assignment policy. We use a genetic algorithm to compute the optimal parameter settings for the policy. We then explore the performance of the policy using the optimal parameters, when the job size distribution is a heavy-tailed bounded Pareto distribution with parameter alpha. We show that TAGS only operates at low inter-arrival rates. At low rates it is very efficient in comparison with other standard policies. At high rates TAGS has to be combined with other policies to achieve good performance. We also show that the performance is nearly symmetrical around the value alpha = 1, with the best performance when alpha = 1
ISBN:1424407044
9781424407040
DOI:10.1109/SCIS.2007.367702