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Scheduling to minimize gaps and power consumption

Published: 09 June 2007 Publication History

Abstract

This paper considers scheduling tasks while minimizing the power consumption of one or more processors, each of which can go to sleep at a fixed cost α. There are two natural versions of this problem, both considered extensively in recent work: minimize the total power consumption (including computation time), or minimize the number of "gaps" in execution. For both versions in a multiprocessor system, we develop a polynomial-time algorithm based on sophisticated dynamic programming. In a generalization of the power-saving problem, where each task can execute in any of a specified set of time intervals, we develop a (1 + 2<over>3 α)-approximation, and show that dependence on α is necessary. In contrast, the analogous multi-interval gap scheduling problem is set-cover hard (and thus not o(lg n)-approximable), even in the special cases of just two intervals per job or just three unit intervals per job. We also prove several other hardness-of-approximation results. Finally, we give an O(√n)-approximation for maximizing throughput given a hard upper bound on the number of gaps.

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Cited By

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  • (2023)Greedy Minimum-Energy SchedulingApproximation and Online Algorithms 10.1007/978-3-031-49815-2_5(59-73)Online publication date: 7-Sep-2023
  • (2022)Balancing Flow Time and Energy ConsumptionProceedings of the 34th ACM Symposium on Parallelism in Algorithms and Architectures10.1145/3490148.3538582(369-380)Online publication date: 11-Jul-2022
  • (2021)Pipe-lining dynamic programming processes to synchronize both the production and the consumption of energyRAIRO - Operations Research10.1051/ro/202109455:4(2359-2383)Online publication date: 9-Aug-2021
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    cover image ACM Conferences
    SPAA '07: Proceedings of the nineteenth annual ACM symposium on Parallel algorithms and architectures
    June 2007
    376 pages
    ISBN:9781595936677
    DOI:10.1145/1248377
    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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    Publication History

    Published: 09 June 2007

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    Author Tags

    1. multiprocessor scheduling
    2. power minimization
    3. sleep state

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    Cited By

    View all
    • (2023)Greedy Minimum-Energy SchedulingApproximation and Online Algorithms 10.1007/978-3-031-49815-2_5(59-73)Online publication date: 7-Sep-2023
    • (2022)Balancing Flow Time and Energy ConsumptionProceedings of the 34th ACM Symposium on Parallelism in Algorithms and Architectures10.1145/3490148.3538582(369-380)Online publication date: 11-Jul-2022
    • (2021)Pipe-lining dynamic programming processes to synchronize both the production and the consumption of energyRAIRO - Operations Research10.1051/ro/202109455:4(2359-2383)Online publication date: 9-Aug-2021
    • (2021)Synchronizing energy production and vehicle routingRAIRO - Operations Research10.1051/ro/202109355:4(2141-2163)Online publication date: 8-Jul-2021
    • (2021)Scheduling with gaps: new models and algorithmsJournal of Scheduling10.1007/s10951-021-00691-wOnline publication date: 21-Jul-2021
    • (2019)Minimizing the Cost of Batch CalibrationsComputing and Combinatorics10.1007/978-3-030-26176-4_7(78-89)Online publication date: 21-Jul-2019
    • (2018)Reducing the energy consumption of large-scale computing systems through combined shutdown policies with multiple constraintsInternational Journal of High Performance Computing Applications10.1177/109434201771453032:1(176-188)Online publication date: 1-Jan-2018
    • (2018)Quantifying the impact of shutdown techniques for energy‐efficient data centersConcurrency and Computation: Practice and Experience10.1002/cpe.447130:17Online publication date: 17-Apr-2018
    • (2017)Maximizing Common Idle Time on Multicore Processors With Shared MemoryIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2017.266997325:7(2095-2108)Online publication date: Jul-2017
    • (2017)A greedy approximation algorithm for minimum-gap schedulingJournal of Scheduling10.1007/s10951-016-0492-y20:3(279-292)Online publication date: 1-Jun-2017
    • Show More Cited By

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