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Energy-Saving SSD Cache Management for Video Servers with Heterogeneous HDDs

Author

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  • Kyungmin Kim

    (Department of Computer Engineering, Inha University, Incheon 22212, Korea)

  • Minseok Song

    (Department of Computer Engineering, Inha University, Incheon 22212, Korea)

Abstract

Dynamic adaptive streaming over HTTP (DASH) technique, the most popular streaming method, requires a large number of hard disk drives (HDDs) to store multiple bitrate versions of many videos, consuming significant energy. A solid-state drive (SSD) can be used to cache popular videos, thus reducing HDD energy consumption by allowing I/O requests to be handled by an SSD, but this requires effective HDD power management due to limited SSD bandwidth. We propose a new SSD cache management scheme to minimize the energy consumption of a video storage system with heterogeneous HDDs. We first present a technique that caches files with the aim of saving more HDD energy as a result of I/O processing on an SSD. Based on this, we propose a new HDD power management algorithm with the goal of increasing the number of HDDs operated in low-power mode while reflecting the heterogeneous HDD power characteristics. For this purpose, it assigns a separate parameter value to each I/O task based on the ratio of HDD energy to bandwidth and greedily selects the I/O tasks handled by the SSD within limits on its bandwidth. Simulation results show that our scheme consumes between 12% and 25% less power than alternative schemes under the same HDD configuration.

Suggested Citation

  • Kyungmin Kim & Minseok Song, 2022. "Energy-Saving SSD Cache Management for Video Servers with Heterogeneous HDDs," Energies, MDPI, vol. 15(10), pages 1-16, May.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3633-:d:816543
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    References listed on IDEAS

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    1. Vojko Matko & Barbara Brezovec, 2018. "Improved Data Center Energy Efficiency and Availability with Multilayer Node Event Processing," Energies, MDPI, vol. 11(9), pages 1-17, September.
    2. Pisinger, David, 1995. "A minimal algorithm for the multiple-choice knapsack problem," European Journal of Operational Research, Elsevier, vol. 83(2), pages 394-410, June.
    3. Damián Fernández-Cerero & Alejandro Fernández-Montes & Francisco Velasco, 2018. "Productive Efficiency of Energy-Aware Data Centers," Energies, MDPI, vol. 11(8), pages 1-17, August.
    4. Pisinger, David, 1995. "An expanding-core algorithm for the exact 0-1 knapsack problem," European Journal of Operational Research, Elsevier, vol. 87(1), pages 175-187, November.
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