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Debunking the myth that tight packing is energy conserving

Published: 04 January 2016 Publication History
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  • Abstract

    Energy takes about half the operational expenses of data centers making energy conservation a critical goal. Fine-grained control over frequency of processors aids in reducing power at the cost of performance degradation. Placing tasks on servers is formulated as a bin packing and tight packing is considered to be energy conserving as idle servers can be shut down. However, we reveal that due to neglecting task deadline and operating at higher frequency leads to higher energy consumption compared to distributing of tasks over larger number of machines at lower frequencies.
    In this paper, we explore how to provision tasks in an energy-optimal manner. We show that task-based provisioning is a variable-sized bin packing problem and analyze energy efficiency of 14 classical and proposed heuristics for different distributions of task deadline requirements. We establish that tight packing leads to higher energy usage and we need to consider the task deadline while provisioning. Our heuristics saves as much as 65% energy compared to existing greedy heuristics for an instance of provisioning.

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    Cited By

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    • (2018)A Load Balancing Approach to Resource Provisioning in Cloud Infrastructure with a Grouping Genetic Algorithm2018 International Conference on Current Trends towards Converging Technologies (ICCTCT)10.1109/ICCTCT.2018.8550885(1-6)Online publication date: Mar-2018

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    cover image ACM Other conferences
    ICDCN '16: Proceedings of the 17th International Conference on Distributed Computing and Networking
    January 2016
    370 pages
    ISBN:9781450340328
    DOI:10.1145/2833312
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 04 January 2016

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    Author Tags

    1. cloud
    2. energy
    3. placements
    4. power
    5. provisioning
    6. task

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    • (2018)A Load Balancing Approach to Resource Provisioning in Cloud Infrastructure with a Grouping Genetic Algorithm2018 International Conference on Current Trends towards Converging Technologies (ICCTCT)10.1109/ICCTCT.2018.8550885(1-6)Online publication date: Mar-2018

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