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Speed Scaling with Multiple Servers under a Sum-Power Constraint

Authors:

Jayakrishnan NairAuthors Info & Claims

ACM SIGMETRICS Performance Evaluation Review, Volume 49, Issue 3

Pages 45 - 50

https://doi.org/10.1145/3529113.3529128

Published: 25 March 2022 Publication History

Abstract

The problem of scheduling jobs and choosing their respective speeds with multiple servers under a sum-power constraint to minimize the flow time + energy is considered.

References

[1]

K. Agrawal, J. Li, K. Lu, and B. Moseley. Scheduling parallelizable jobs online to minimize the maximum flow time. In Proceedings of the 28th ACM Symposium on Parallelism in Algorithms and Architectures, pages 195--205, 2016.

Digital Library

[2]

N. Bansal, H.-L. Chan, and K. Pruhs. Speed scaling with an arbitrary power function. In Proceedings of the twentieth annual ACM-SIAM symposium on discrete algorithms, pages 693--701. SIAM, 2009.

Digital Library

[3]

B. Berg, J.-P. Dorsman, and M. Harchol-Balter. Towards optimality in parallel scheduling. Proceedings of the ACM on Measurement and Analysis of Computing Systems, 1(2):1--30, 2017.

Digital Library

[4]

B. Berg, R. Vesilo, and M. Harchol-Balter. heSRPT: Optimal scheduling of parallel jobs with known sizes. SIGMETRICS Perform. Evaluation Rev., 47(2):18--20, 2019.

Digital Library

[5]

T. Condie, N. Conway, P. Alvaro, J. M. Hellerstein, K. Elmeleegy, and R. Sears. Mapreduce online. In NSDI, volume 10, page 20, 2010.

[6]

N. R. Devanur and Z. Huang. Primal dual gives almost optimal energy-efficient online algorithms. ACM Transactions on Algorithms (TALG), 14(1):1--30, 2017.

[7]

J. Edmonds. Scheduling in the dark. Theoretical Computer Science, 235(1):109--141, 2000.

Digital Library

[8]

J. Edmonds and K. Pruhs. Scalably scheduling processes with arbitrary speedup curves. In Proceedings of the twentieth annual ACM-SIAM symposium on Discrete algorithms, pages 685--692. SIAM, 2009.

Digital Library

[9]

A. Gupta, S. Im, R. Krishnaswamy, B. Moseley, and K. Pruhs. Scheduling heterogeneous processors isn't as easy as you think. In Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete algorithms, pages 1242--1253. SIAM, 2012.

Digital Library

[10]

M. Harchol-Balter. Open problems in queueing theory inspired by datacenter computing. Queueing Systems, 97(1):3--37, 2021.

Digital Library

[11]

S. Im, B. Moseley, K. Pruhs, and E. Torng. Competitively scheduling tasks with intermediate parallelizability. ACM Transactions on Parallel Computing (TOPC), 3(1):1--19, 2016.

Digital Library

[12]

M. Isard, M. Budiu, Y. Yu, A. Birrell, and D. Fetterly. Dryad: distributed data-parallel programs from sequential building blocks. In ACM SIGOPS operating systems review, volume 41, pages 59--72. ACM, 2007.

Digital Library

[13]

S.-H. Lin, M. Paolieri, C.-F. Chou, and L. Golubchik. A model-based approach to streamlining distributed training for asynchronous sgd. In 2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), pages 306--318. IEEE, 2018.

[14]

R. Ren and X. Tang. Clairvoyant dynamic bin packing for job scheduling with minimum server usage time. In Proceedings of the 28th ACM SPAA, pages 227--237, 2016.

Digital Library

[15]

C. J. Rossbach, Y. Yu, J. Currey, J.-P. Martin, and D. Fetterly. Dandelion: a compiler and runtime for heterogeneous systems. In Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles, pages 49--68. ACM, 2013.

Digital Library

[16]

R. Vaze and J. Nair. Speed scaling with multiple servers under a sum power constraint. In https://arxiv.org/abs/2108.06935.

[17]

R. Vaze and J. Nair. Multiple server SRPT with speed scaling is competitive. IEEE/ACM Transactions on Networking, 28(4):1739--1751, 2020.

Digital Library

[18]

R. Vaze and J. Nair. Network speed scaling. Performance Evaluation, 144:102145, 2020.

[19]

A. Verma, L. Pedrosa, M. Korupolu, D. Oppenheimer, E. Tune, and J. Wilkes. Large-scale cluster management at google with borg. In Proceedings of the Tenth European Conference on Computer Systems, pages 1--17, 2015.

Digital Library

Cited By

Liang W(2022)Optimization of the PHP Interactive Interface of a College Online Educational Platform in a Quantum Heterogeneous Cloud Environment2022 International Conference on Edge Computing and Applications (ICECAA)10.1109/ICECAA55415.2022.9936458(79-82)Online publication date: 13-Oct-2022
https://doi.org/10.1109/ICECAA55415.2022.9936458

Index Terms

Speed Scaling with Multiple Servers under a Sum-Power Constraint
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

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM SIGMETRICS Performance Evaluation Review

ACM SIGMETRICS Performance Evaluation Review Volume 49, Issue 3

December 2021

77 pages

ISSN:0163-5999

DOI:10.1145/3529113

Editor:
Zhenhua Liu
Stony Brook University

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Copyright © 2022 Copyright is held by the owner/author(s).

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 March 2022

Published in SIGMETRICS Volume 49, Issue 3

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Cited By

Liang W(2022)Optimization of the PHP Interactive Interface of a College Online Educational Platform in a Quantum Heterogeneous Cloud Environment2022 International Conference on Edge Computing and Applications (ICECAA)10.1109/ICECAA55415.2022.9936458(79-82)Online publication date: 13-Oct-2022
https://doi.org/10.1109/ICECAA55415.2022.9936458

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