research-article

Getting the best response for your erg

Authors:

Patchrawat Uthaisombut,

Gerhard WoegingerAuthors Info & Claims

ACM Transactions on Algorithms (TALG), Volume 4, Issue 3

Article No.: 38, Pages 1 - 17

https://doi.org/10.1145/1367064.1367078

Published: 04 July 2008 Publication History

Abstract

We consider the speed scaling problem of minimizing the average response time of a collection of dynamically released jobs subject to a constraint A on energy used. We propose an algorithmic approach in which an energy optimal schedule is computed for a huge A, and then the energy optimal schedule is maintained as A decreases. We show that this approach yields an efficient algorithm for equi-work jobs. We note that the energy optimal schedule has the surprising feature that the job speeds are not monotone functions of the available energy. We then explain why this algorithmic approach is problematic for arbitrary work jobs. Finally, we explain how to use the algorithm for equi-work jobs to obtain an algorithm for arbitrary work jobs that is O(1)-approximate with respect to average response time, given an additional factor of (1 + ϵ) energy.

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Cited By

Zakharova YSakhno M(2024)Adaptive Genetic Algorithm with Optimized Operators for Scheduling in Computer SystemsIntelligent Information Processing XII10.1007/978-3-031-57808-3_23(317-328)Online publication date: 6-Apr-2024
https://doi.org/10.1007/978-3-031-57808-3_23
Kononov AZakharova Y(2022)Speed scaling scheduling of multiprocessor jobs with energy constraint and makespan criterionJournal of Global Optimization10.1007/s10898-021-01115-x83:3(539-564)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1007/s10898-021-01115-x
Zhang WWen YLai LLiu FFan R(2021)Cost Optimal Data Center Servers: A Voltage Scaling ApproachIEEE Transactions on Cloud Computing10.1109/TCC.2018.28448239:1(118-130)Online publication date: 1-Jan-2021
https://doi.org/10.1109/TCC.2018.2844823
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Index Terms

Getting the best response for your erg
1. Theory of computation
  1. Design and analysis of algorithms
    1. Approximation algorithms analysis
      1. Scheduling algorithms
    2. Online algorithms
      1. Online learning algorithms
        Scheduling algorithms
  2. Theory and algorithms for application domains
    1. Machine learning theory
      1. Reinforcement learning
        Sequential decision making

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Published In

cover image ACM Transactions on Algorithms

ACM Transactions on Algorithms Volume 4, Issue 3

June 2008

270 pages

ISSN:1549-6325

EISSN:1549-6333

DOI:10.1145/1367064

Issue’s Table of Contents

Copyright © 2008 ACM.

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 July 2008

Accepted: 01 May 2007

Revised: 01 March 2007

Received: 01 December 2005

Published in TALG Volume 4, Issue 3

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Cited By

Zakharova YSakhno M(2024)Adaptive Genetic Algorithm with Optimized Operators for Scheduling in Computer SystemsIntelligent Information Processing XII10.1007/978-3-031-57808-3_23(317-328)Online publication date: 6-Apr-2024
https://doi.org/10.1007/978-3-031-57808-3_23
Kononov AZakharova Y(2022)Speed scaling scheduling of multiprocessor jobs with energy constraint and makespan criterionJournal of Global Optimization10.1007/s10898-021-01115-x83:3(539-564)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1007/s10898-021-01115-x
Zhang WWen YLai LLiu FFan R(2021)Cost Optimal Data Center Servers: A Voltage Scaling ApproachIEEE Transactions on Cloud Computing10.1109/TCC.2018.28448239:1(118-130)Online publication date: 1-Jan-2021
https://doi.org/10.1109/TCC.2018.2844823
Kononov AKovalenko Y(2021)Minimizing Total Completion Time in Multiprocessor Job Systems with Energy ConstraintMathematical Optimization Theory and Operations Research10.1007/978-3-030-77876-7_18(267-279)Online publication date: 14-Jun-2021
https://doi.org/10.1007/978-3-030-77876-7_18
Singh PKhan BVidyarthi AHaes Alhelou HSiano P(2019)Energy-Aware Online Non-Clairvoyant Scheduling Using Speed Scaling with Arbitrary Power FunctionApplied Sciences10.3390/app90714679:7(1467)Online publication date: 8-Apr-2019
https://doi.org/10.3390/app9071467
Angel EBampis EKacem FLetsios D(2019)Speed scaling on parallel processors with migrationJournal of Combinatorial Optimization10.1007/s10878-018-0352-037:4(1266-1282)Online publication date: 1-May-2019
https://dl.acm.org/doi/10.1007/s10878-018-0352-0
Pruhs K(2019)Green Computing AlgorithmicsComputing and Software Science10.1007/978-3-319-91908-9_10(161-183)Online publication date: 2019
https://doi.org/10.1007/978-3-319-91908-9_10
Megow NVerschae J(2018)Dual Techniques for Scheduling on a Machine with Varying SpeedSIAM Journal on Discrete Mathematics10.1137/16M105589X32:3(1541-1571)Online publication date: Jan-2018
https://doi.org/10.1137/16M105589X
Agrawal KGilbert S(2018)The Power to Schedule a Parallel Program2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS.2018.00028(182-193)Online publication date: May-2018
https://doi.org/10.1109/IPDPS.2018.00028
Carrasco RIyengar GStein C(2018)Resource cost aware schedulingEuropean Journal of Operational Research10.1016/j.ejor.2018.02.059269:2(621-632)Online publication date: Sep-2018
https://doi.org/10.1016/j.ejor.2018.02.059
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