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A Measurement-Based Model to Predict the Performance Impact of System Modifications: A Case Study
January 1995 (vol. 6 no. 1)
pp. 28-40

Abstract—This paper presents a performance case study of parallel jobs executing in real multi-user workloads. The study is based on a measurement-based model capable of predicting the completion time distribution of the jobs executing under real workloads. The model constructed is also capable of predicting the effects of system design changes on application performance. The model is a finite-state, discrete-time Markov model with rewards and costs associated with each state. The Markov states are defined from real measurements and represent system/workload states in which the machine has operated. This paper places special emphasis on choosing the correct number of states to represent the workload measured. Specifically, the performance of computationally-bound, parallel applications executing in real workloads on an Alliant FX/80 is evaluated. The constructed model is used to evaluate scheduling policies, the performance effects of multiprogramming overhead, and the scalability of the Alliant FX/80 in real workloads. The model identifies a number of available scheduling policies which would improve the response time of parallel jobs. In addition, the model predicts that doubling the number of processors in the current configuration would only improve response time for a typical parallel application by 25%. The model recommends a different processor configuration to more fully utilize extra processors. This paper also presents empirical results which validate the model created.

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Citation:
Robert T. Dimpsey, Ravishankar K. Iyer, "A Measurement-Based Model to Predict the Performance Impact of System Modifications: A Case Study," IEEE Transactions on Parallel and Distributed Systems, vol. 6, no. 1, pp. 28-40, Jan. 1995, doi:10.1109/71.363413
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