Operational analysis of processor speed scaling

Authors:
Kai Shen;Alex Zhang;Terence Kelly;Christopher Stewart
Affiliations:
University of Rochester, Rochester, NY, USA;Hewlett-Packard Laboratories, Palo Alto, CA, USA;Hewlett-Packard Laboratories, Palo Alto, CA, USA;University of Rochester, Rochester, NY, USA
Venue:
Proceedings of the twentieth annual symposium on Parallelism in algorithms and architectures
Year:
2008

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Abstract

This brief announcement presents a pair of performance laws that bound the change in aggregate job queueing time that results when the processor speed changes in a parallel computing system. Our laws require only lightweight passive external observations of a black-box system and they apply to many commonly employed scheduling policies. By predicting the application-level performance impact of processing speed adjustments in parallel processors, including traditional SMPs and now increasingly ubiquitous multicore processors, our laws address problems ranging from capacity planning to dynamic resource allocation. Finally, our results show that operational analysis---an approach to performance analysis traditionally associated with commercial transaction processing systems---usefully complements existing parallel performance analysis techniques.