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Performance prediction in production environments

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2 Author(s)
Schopf, Jennifer M. ; Dept. of Comput. Sci. & Eng., California Univ., San Diego, La Jolla, CA, USA ; Berman, F.

Accurate performance predictions are difficult to achieve for parallel applications executing on production distributed systems. Conventional point-valued performance parameters and prediction models are often inaccurate since they can only represent one point in a range of possible behaviors. The authors address this problem by allowing characteristic application and system data to be represented by a set of possible values and their probabilities, which they call stochastic values. They give a practical methodology for using stochastic values as parameters to adaptable performance prediction models. They demonstrate their usefulness for a distributed SOR application, showing stochastic values to be more effective than single (point) values in predicting the range of application behavior that can occur during execution in production environments

Published in:

Parallel Processing Symposium, 1998. IPPS/SPDP 1998. Proceedings of the First Merged International ... and Symposium on Parallel and Distributed Processing 1998

Date of Conference:

30 Mar-3 Apr 1998

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