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    Utility-based resource management in an oversubscribed energy-constrained heterogeneous environment executing parallel applications

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    parco_manuscript.pdf
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    Description:
    Final Accepted Manuscript
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    Author
    Machovec, Dylan
    Khemka, Bhavesh
    Kumbhare, Nirmal
    Pasricha, Sudeep
    Maciejewski, Anthony A.
    Siegel, Howard Jay
    Akoglu, Ali
    Koenig, Gregory A.
    Hariri, Salim
    Tunc, Cihan
    Wright, Michael
    Hilton, Marcia
    Rambharos, Rajendra
    Blandin, Christopher
    Fargo, Farah
    Louri, Ahmed
    Imam, Neena
    Show allShow less
    Affiliation
    Univ Arizona, Dept Elect & Comp Engn
    Issue Date
    2019-04
    Keywords
    Heterogeneous computing
    Energy-aware computing
    Utility functions
    Resource management heuristics
    Parallel tasks
    Scheduling
    
    Metadata
    Show full item record
    Publisher
    ELSEVIER SCIENCE BV
    Citation
    Machovec, D., Khemka, B., Kumbhare, N., Pasricha, S., Maciejewski, A. A., Siegel, H. J., ... & Wright, M. (2017). Utility-based resource management in an oversubscribed energy-constrained heterogeneous environment executing parallel applications. Parallel Computing.
    Journal
    PARALLEL COMPUTING
    Rights
    © 2017 Elsevier B.V. All rights reserved.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    The worth of completing parallel tasks is modeled using utility functions, which monotonically-decrease with time and represent the importance and urgency of a task. These functions define the utility earned by a task at the time of its completion. The performance of a computing system is measured as the total utility earned by all completed tasks over some interval of time (e.g., 24 h). We have designed, analyzed, and compared the performance of a set of heuristic techniques to maximize system performance when scheduling dynamically arriving parallel tasks onto a high performance computing (HPC) system that is oversubscribed and energy constrained. We consider six utility-aware heuristics and four existing heuristics for comparison. A new concept of temporary place holders is compared with scheduling using permanent reservations. We also present a novel energy filtering technique that constrains the maximum energy-per-resource used by each task. We conducted a simulation study to evaluate the performance of these heuristics and techniques in multiple energy-constrained oversubscribed HPC environments. We conduct an experiment with a subset of the heuristics on a physical testbed system for one scheduling scenario. We demonstrate that our proposed utility-aware resource management heuristics are able to significantly outperform existing techniques. (C) 2017 Elsevier B.V. All rights reserved.
    Note
    24 month embargo; published online: 7 November 2017
    ISSN
    01678191
    DOI
    10.1016/j.parco.2017.11.005
    Version
    Final accepted manuscript
    Sponsors
    U.S. Department of Energy [DE-AC05-00OR22725]; Department of Energy; Oak Ridge National Laboratory for the Department of Defense (DoD) [4000108022]; National Science Foundation (NSF) [CCF-1302693, CCF-1547036, CCF-1547035]; NSF [CNS-0923386]
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/S0167819117301862
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.parco.2017.11.005
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