IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i10p3633-d816543.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/10/3633/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/10/3633/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wishon, Christopher & Villalobos, J. Rene, 2016. "Robust efficiency measures for linear knapsack problem variants," European Journal of Operational Research, Elsevier, vol. 254(2), pages 398-409.
    2. Higgins Michael J. & Rivest Ronald L. & Stark Philip B., 2011. "Sharper p-Values for Stratified Election Audits," Statistics, Politics and Policy, De Gruyter, vol. 2(1), pages 1-37, October.
    3. Kateryna Czerniachowska, 2022. "A genetic algorithm for the retail shelf space allocation problem with virtual segments," OPSEARCH, Springer;Operational Research Society of India, vol. 59(1), pages 364-412, March.
    4. Hoto, Robinson & Arenales, Marcos & Maculan, Nelson, 2007. "The one dimensional Compartmentalised Knapsack Problem: A case study," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1183-1195, December.
    5. Mavrotas, George & Figueira, José Rui & Florios, Kostas, 2009. "Solving the bi-objective multidimensional knapsack problem exploiting the concept of core," MPRA Paper 105087, University Library of Munich, Germany.
    6. Haahr, J.T. & Lusby, R.M. & Wagenaar, J.C., 2015. "A Comparison of Optimization Methods for Solving the Depot Matching and Parking Problem," ERIM Report Series Research in Management ERS-2015-013-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    7. Christian Tipantuña & Xavier Hesselbach, 2020. "NFV-Enabled Efficient Renewable and Non-Renewable Energy Management: Requirements and Algorithms," Future Internet, MDPI, vol. 12(10), pages 1-31, October.
    8. Subhash C. Sarin & Hanif D. Sherali & Seon Ki Kim, 2014. "A branch‐and‐price approach for the stochastic generalized assignment problem," Naval Research Logistics (NRL), John Wiley & Sons, vol. 61(2), pages 131-143, March.
    9. Li, Xin & Qian, Zhuzhong & You, Ilsun & Lu, Sanglu, 2014. "Towards cost efficient mobile service and information management in ubiquitous environment with cloud resource scheduling," International Journal of Information Management, Elsevier, vol. 34(3), pages 319-328.
    10. David Pisinger, 2000. "A Minimal Algorithm for the Bounded Knapsack Problem," INFORMS Journal on Computing, INFORMS, vol. 12(1), pages 75-82, February.
    11. Yanasse, Horacio Hideki & Pinto Lamosa, Maria Jose, 2007. "An integrated cutting stock and sequencing problem," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1353-1370, December.
    12. Kateryna Czerniachowska & Marcin Hernes, 2021. "Shelf Space Allocation for Specific Products on Shelves Selected in Advance," European Research Studies Journal, European Research Studies Journal, vol. 0(3), pages 316-334.
    13. Martello, Silvano & Pisinger, David & Toth, Paolo, 2000. "New trends in exact algorithms for the 0-1 knapsack problem," European Journal of Operational Research, Elsevier, vol. 123(2), pages 325-332, June.
    14. Christensen, Tue R.L. & Labbé, Martine, 2015. "A branch-cut-and-price algorithm for the piecewise linear transportation problem," European Journal of Operational Research, Elsevier, vol. 245(3), pages 645-655.
    15. Shuaian Wang & Dan Zhuge & Lu Zhen & Chung-Yee Lee, 2021. "Liner Shipping Service Planning Under Sulfur Emission Regulations," Transportation Science, INFORMS, vol. 55(2), pages 491-509, March.
    16. M Hifi & M Michrafy, 2006. "A reactive local search-based algorithm for the disjunctively constrained knapsack problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(6), pages 718-726, June.
    17. Sbihi, Abdelkader, 2010. "A cooperative local search-based algorithm for the Multiple-Scenario Max-Min Knapsack Problem," European Journal of Operational Research, Elsevier, vol. 202(2), pages 339-346, April.
    18. Yanhong Feng & Xu Yu & Gai-Ge Wang, 2019. "A Novel Monarch Butterfly Optimization with Global Position Updating Operator for Large-Scale 0-1 Knapsack Problems," Mathematics, MDPI, vol. 7(11), pages 1-31, November.
    19. Jakob Puchinger & Günther R. Raidl & Ulrich Pferschy, 2010. "The Multidimensional Knapsack Problem: Structure and Algorithms," INFORMS Journal on Computing, INFORMS, vol. 22(2), pages 250-265, May.
    20. Tue R. L. Christensen & Kim Allan Andersen & Andreas Klose, 2013. "Solving the Single-Sink, Fixed-Charge, Multiple-Choice Transportation Problem by Dynamic Programming," Transportation Science, INFORMS, vol. 47(3), pages 428-438, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3633-:d:816543. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.