Markov Model Based Disk Power Management for Data Intensive Workloads

In order to meet the increasing demands of present and upcoming data-intensive computer applications, there has been a major shift in the disk subsystem, which now consists of more disks with higher storage capacities and higher rotational speeds. These have made the disk subsystem a major consumer...

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Veröffentlicht in:2009 9th IEEE International Symposium on Cluster Computing and the Grid S. 76 - 83
Hauptverfasser: Garg, R., Seung Woo Son, Kandemir, M., Raghavan, P., Prabhakar, R.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.05.2009
Schlagworte:
Batteries
Costs
Energy consumption
Energy management
Grid computing
Markov
Mathematical model
Mathematics
mulit-speed
power
Power system modeling
prediction
Predictive models
Spinning
ISBN:1424439353, 9781424439355
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Zusammenfassung:In order to meet the increasing demands of present and upcoming data-intensive computer applications, there has been a major shift in the disk subsystem, which now consists of more disks with higher storage capacities and higher rotational speeds. These have made the disk subsystem a major consumer of power, making disk power management an important issue. People have considered the option of spinning down the disk during periods of idleness or serving the requests at lower rotational speeds when performance is not an issue. Accurately predicting future disk idle periods is crucial to such schemes. This paper presents a novel disk-idleness prediction mechanism based on Markov models and explains how this mechanism can be used in conjunction with a three-speed disk. Our experimental evaluation using a diverse set of workloads indicates that (i) prediction accuracies achieved by the proposed scheme are very good (87.5% on average); (ii) it generates significant energy savings over the traditional power-saving method of spinning down the disk when idle (35.5% on average); (iii) it performs better than a previously proposed multi-speed disk management scheme (19% on average); and (iv) the performance penalty is negligible (less than 1% on average). Overall, our implementation and experimental evaluation using both synthetic disk traces and traces extracted from real applications demonstrate the feasibility of a Markov-model-based approach to saving disk power.
ISBN:1424439353
9781424439355
DOI:10.1109/CCGRID.2009.67