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PAP: power aware prediction based framework to reduce disk energy consumption

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Abstract

Disk-based storage subsystems account for a significant portion of the energy consumption in both low and high end servers. Therefore, there is a dire need to reduce the server power consumption of the hard disks. In this work, the power-aware framework has been proposed, which efficiently switches the disk into standby, active and idle states, leading to the least power consumption. Firstly, the trace of a real-world application has been generated and processed. The frequently used queries from the trace have been analyzed and prefetched in SSD cache using the data placement policy which lead to 78.5% cache hits. Subsequently, the idle time threshold policy has been executed, which regularly monitors and compares the disk idle time with its threshold value. Later, the request arrival threshold policy predicts the breakeven time using the ensemble machine learning model, which yields 87% accuracy with 3.5% average error rate. Only upon exceeding the threshold values, the disk would smartly be placed in the standby mode; otherwise, it would remain in the idle state to avoid the high power spins in case of frequent requests. Finally, the experimental results have been validated with the existing benchmarks using SSD as a cache, which leads to 75% average power savings.

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Notes

  1. https://www.bmj.com/about-bmj/resources-readers/publications/statistics-square-one/11-correlation-and-regression.

  2. http://scikit-learn.org/stable/modules/model_evaluation.html.

  3. http://www.dublinked.ie.

  4. k-fold cross-validation quarantines the subsets of the training data during the learning process. It randomly splits the data into k subsets termed as folds.

  5. Write off-loading allows write requests on spun-down disks to be temporarily redirected to persistent storage elsewhere in the data center.

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Acknowledgements

One of the authors, Sumedha Arora offers the sincerest gratitude to the Council of Scientific and Industrial Research (CSIR), Government of India, for funding the research and providing required resources to carry out this research work with the Ack. No. 143253/2K17/1 and File No. 09/677(0030)/2018-EMR-I.

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  1. Computer Science and Engineering Department, Patiala, India

    Sumedha Arora & Anju Bala

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  1. Sumedha Arora
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Correspondence to Sumedha Arora.

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Arora, S., Bala, A. PAP: power aware prediction based framework to reduce disk energy consumption. Cluster Comput 23, 3157–3174 (2020). https://doi.org/10.1007/s10586-020-03077-3

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