Authors: Vasanth Venkatachalam, Michael Franz, Christian W. Probst
Addresses: Donald Bren School of Information and Computer Science, University of California, Irvine, CA, USA. ' Donald Bren School of Information and Computer Science, University of California, Irvine, CA, USA. ' Informatics and Mathematical Modelling, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
Abstract: Many dynamic voltage scaling algorithms rely on measuring hardware events (such as cache misses) for predicting how much a workload can be slowed down with acceptable performance loss. The events measured, however, are at best indirectly related to execution time and clock frequency. By relating these two indicators logically, we propose a new way of predicting a workload|s compute-boundedness that is based on direct observation, and only requires measuring the total execution cycles for the two highest clock frequencies. Our predictor can be used to develop dynamic voltage scaling algorithms that are more system-aware than current approaches.
Keywords: dynamic voltage scaling; DVS; performance estimation; virtual machines.
International Journal of Embedded Systems, 2007 Vol.3 No.1/2, pp.17 - 30
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