Predictive application-performance modeling in a computational grid environment

This paper describes and evaluates the application of three local learning algorithms-nearest-neighbor, weighted-average, and locally-weighted polynomial regression-for the prediction of run-specific resource-usage on the basis of run-time input parameters supplied to tools. A two-level knowledge ba...

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Published inProceedings of the IEEE International Symposium on High Performance Distributed Computing pp. 47 - 54
Main Authors Kapadia, N.H., Fortes, J.A.B., Brodley, C.E.
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 1999
Subjects
Application software
Computational modeling
Computer networks
Distributed computing
Grid computing
Knowledge engineering
Polynomials
Predictive models
Resource management
Runtime environment
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Summary:This paper describes and evaluates the application of three local learning algorithms-nearest-neighbor, weighted-average, and locally-weighted polynomial regression-for the prediction of run-specific resource-usage on the basis of run-time input parameters supplied to tools. A two-level knowledge base allows the learning algorithms to track short-term fluctuations in the performances of computing systems, and the use of instance editing techniques improves the scalability of the performance-modeling system. The learning algorithms assist PUNCH, a network-computing system at Purdue University, in emulating an ideal user in terms of its resource management and usage policies.
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ISBN:0780356810
9780780356818
ISSN:1082-8907
DOI:10.1109/HPDC.1999.805281