research-article

Scheduling jobs with varying parallelizability to reduce variance

Authors:

Ravishankar Krishnaswamy,

Benjamin Moseley,

Kirk PruhsAuthors Info & Claims

SPAA '10: Proceedings of the twenty-second annual ACM symposium on Parallelism in algorithms and architectures

Pages 11 - 20

https://doi.org/10.1145/1810479.1810482

Published: 13 June 2010 Publication History

Abstract

We give a (2+ε)-speed O(1)-competitive algorithm for scheduling jobs with arbitrary speed-up curves for the l₂ norm of flow. We give a similar result for the broadcast setting with varying page sizes.

References

[1]

Nikhil Bansal, Ravishankar Krishnaswamy, and Viswanath Nagarajan. Better scalable algorithms for broadcast scheduling. Technical report, Carnegie Mellon University, 2009.

[2]

Nikhil Bansal, Ravishankar Krishnaswamy, and Viswanath Nagarajan. Better scalable algorithms for broadcast scheduling. Technical report, Carnegie Mellon University, 2009.

[3]

Luca Becchetti and Stefano Leonardi. Nonclairvoyant scheduling to minimize the total flow time on single and parallel machines. J. ACM, 51(4):517--539, 2004.

Digital Library

[4]

Ho-Leung Chan, Jeff Edmonds, and Kirk Pruhs. Speed scaling of processes with arbitrary speedup curves on a multiprocessor. In SPAA, pages 1--10, 2009.

Digital Library

[5]

Jeff Edmonds. Scheduling in the dark. Theor. Comput. Sci., 235(1):109--141, 2000.

Digital Library

[6]

Jeff Edmonds, Donald D. Chinn, Tim Brecht, and Xiaotie Deng. Non-clairvoyant multiprocessor scheduling of jobs with changing execution characteristics. J. Scheduling, 6(3):231--250, 2003.

Digital Library

[7]

Jeff Edmonds and Kirk Pruhs. Multicast pull scheduling: When fairness is fine. Algorithmica, 36(3):315--330, 2003.

Digital Library

[8]

Jeff Edmonds and Kirk Pruhs. Scalably scheduling processes with arbitrary speedup curves. In SODA, pages 685--692, 2009.

Digital Library

[9]

Sungjin Im and Benjamin Moseley. An online scalable algorithm for average flow time in broadcast scheduling. In ACM--SIAM Symposium on Discrete Algorithms, page To Appear, 2010.

Digital Library

[10]

Bala Kalyanasundaram and Kirk Pruhs. Speed is as powerful as clairvoyance. J. ACM, 47(4):617--643, 2000.

Digital Library

[11]

Bala Kalyanasundaram and Kirk Pruhs. Minimizing flow time nonclairvoyantly. J. ACM, 50(4):551--567, 2003.

Digital Library

[12]

Rajeev Motwani, Steven Phillips, and Eric Torng. Nonclairvoyant scheduling. Theorertical Computer Science, 130(1):17--47, 1994.

Digital Library

[13]

Kirk Pruhs. Competitive online scheduling for server systems. SIGMETRICS Performance Evaluation Review, 34(4):52--58, 2007.

Digital Library

[14]

Kirk Pruhs, Jiri Sgall, and Eric Torng. Online scheduling. In Handbook on Scheduling. CRC Press, 2004.

[15]

Julien Robert and Nicolas Schabanel. Non-clairvoyant batch sets scheduling: Fairness is fair enough. In European Symposium on Algorithms, pages 741--753, 2007.

Digital Library

[16]

Julien Robert and Nicolas Schabanel. Non-clairvoyant scheduling with precedence constraints. In ACM-SIAM Symposium on Discrete Algorithms, pages 491--500, 2008.

Digital Library

[17]

Abraham Silberschatz and Peter Galvin. Operating System Concepts, 4th edition. Addison-Wesley, 1994.

Digital Library

Cited By

Moselely BZhang RZhao S(2022)Online scheduling of parallelizable jobs in the directed acyclic graphs and speed-up curves modelsTheoretical Computer Science10.1016/j.tcs.2022.10.005Online publication date: Oct-2022
https://doi.org/10.1016/j.tcs.2022.10.005
Wang TQian ZJiao LLi XLu S(2020)GeoClone: Online Task Replication and Scheduling for Geo-Distributed Analytics under Uncertainties2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS)10.1109/IWQoS49365.2020.9212862(1-10)Online publication date: Jun-2020
https://doi.org/10.1109/IWQoS49365.2020.9212862
Xu Hde Veciana GLau WZhou K(2019)Online Job Scheduling with Redundancy and Opportunistic CheckpointingIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2018.287113530:4(897-909)Online publication date: 18-Jul-2019
https://dl.acm.org/doi/10.1109/TPDS.2018.2871135
Show More Cited By

Index Terms

Scheduling jobs with varying parallelizability to reduce variance
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

Recommendations

Modified Rate-Monotonic Algorithm for Scheduling Periodic Jobs with Deferred Deadlines

The deadline of a request is the time instant at which its execution must complete. The deadline of the request in any period of a job with deferred deadline is some time instant after the end of the period. The authors describe a semi-static priority-...
Scheduling parallel jobs to minimize the makespan

We consider the NP-hard problem of scheduling parallel jobs with release dates on identical parallel machines to minimize the makespan. A parallel job requires simultaneously a prespecified, job-dependent number of machines when being processed. We ...
Generalization of EDF and LLF: Identifying All Optimal Online Algorithms for Minimizing Maximum Lateness

It is well known that the Earliest-Deadline-First (EDF) and the Least-Laxity-First (LLF) algorithms are optimal algorithms for the problem of preemptively scheduling jobs that arrive over time on a single machine to minimize the maximum lateness (1|rj,...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SPAA '10: Proceedings of the twenty-second annual ACM symposium on Parallelism in algorithms and architectures

June 2010

378 pages

ISBN:9781450300797

DOI:10.1145/1810479

General Chairs:
Friedhelm Meyer auf der Heide
University of Paderborn, Germany
,
Cynthia Phillips
Sandia National Laboratories, USA

Copyright © 2010 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 June 2010

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SPAA 10

Sponsor:

SPAA 10: 22nd ACM Symposium on Parallelism in Algorithms and Architectures

June 13 - 15, 2010

Santorini, Thira, Greece

Acceptance Rates

Overall Acceptance Rate 447 of 1,461 submissions, 31%

Upcoming Conference

SPAA '25

Sponsor:
sigact
sigact

37th ACM Symposium on Parallelism in Algorithms and Architectures

July 28 - August 1, 2025

Portland , OR , USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

27
Total Citations
View Citations
285
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 25 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Moselely BZhang RZhao S(2022)Online scheduling of parallelizable jobs in the directed acyclic graphs and speed-up curves modelsTheoretical Computer Science10.1016/j.tcs.2022.10.005Online publication date: Oct-2022
https://doi.org/10.1016/j.tcs.2022.10.005
Wang TQian ZJiao LLi XLu S(2020)GeoClone: Online Task Replication and Scheduling for Geo-Distributed Analytics under Uncertainties2020 IEEE/ACM 28th International Symposium on Quality of Service (IWQoS)10.1109/IWQoS49365.2020.9212862(1-10)Online publication date: Jun-2020
https://doi.org/10.1109/IWQoS49365.2020.9212862
Xu Hde Veciana GLau WZhou K(2019)Online Job Scheduling with Redundancy and Opportunistic CheckpointingIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2018.287113530:4(897-909)Online publication date: 18-Jul-2019
https://dl.acm.org/doi/10.1109/TPDS.2018.2871135
Agrawal KLee ILi JLu KMoseley B(2019)Practically Efficient Scheduler for Minimizing Average Flow Time of Parallel Jobs2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS.2019.00024(134-144)Online publication date: May-2019
https://doi.org/10.1109/IPDPS.2019.00024
Ebrahimi RMccauley SMoseley B(2018)Scheduling Parallel Jobs Online with Convex and Concave ParallelizabilityTheory of Computing Systems10.1007/s00224-016-9722-062:2(304-318)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s00224-016-9722-0
Im SMoseley BKlein P(2017)Fair scheduling via iterative quasi-uniform samplingProceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms10.5555/3039686.3039857(2601-2615)Online publication date: 16-Jan-2017
https://dl.acm.org/doi/10.5555/3039686.3039857
Xu HLau WYang Zde Veciana GHou H(2017)Mitigating Service Variability in MapReduce Clusters via Task Cloning: A Competitive AnalysisIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2017.268976728:10(2866-2880)Online publication date: 1-Oct-2017
https://doi.org/10.1109/TPDS.2017.2689767
Xu QZheng R(2017)When data acquisition meets data analytics: A distributed active learning framework for optimal budgeted mobile crowdsensingIEEE INFOCOM 2017 - IEEE Conference on Computer Communications10.1109/INFOCOM.2017.8057034(1-9)Online publication date: May-2017
https://doi.org/10.1109/INFOCOM.2017.8057034
Xu HHuang HChen SZhao G(2017)Scalable software-defined networking through hybrid switchingIEEE INFOCOM 2017 - IEEE Conference on Computer Communications10.1109/INFOCOM.2017.8057001(1-9)Online publication date: May-2017
https://doi.org/10.1109/INFOCOM.2017.8057001
Xu Hde Veciana GLau W(2017)Addressing job processing variability through redundant execution and opportunistic checkpointing: A competitive analysisIEEE INFOCOM 2017 - IEEE Conference on Computer Communications10.1109/INFOCOM.2017.8056972(1-9)Online publication date: May-2017
https://doi.org/10.1109/INFOCOM.2017.8056972
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten