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Predictive application-performance modeling in a computational grid environment

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3 Author(s)
N. H. Kapadia ; Sch. of Electr. & Comput. Eng., Purdue Univ., West Lafayette, IN, USA ; J. A. B. Fortes ; C. E. Brodley

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

Published in:

High Performance Distributed Computing, 1999. Proceedings. The Eighth International Symposium on

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