Data Centers Job Scheduling with Deep Reinforcement Learning

Sisheng Liang; Zhou Yang; Fang Jin; Yong Chen

doi:10.1007/978-3-030-47436-2_68

Data Centers Job Scheduling with Deep Reinforcement Learning

Sisheng Liang, Zhou Yang, Fang Jin, Yong Chen

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

14 Scopus citations

Abstract

Efficient job scheduling on data centers under heterogeneous complexity is crucial but challenging since it involves the allocation of multi-dimensional resources over time and space. To adapt the complex computing environment in data centers, we proposed an innovative Advantage Actor-Critic (A2C) deep reinforcement learning based approach called A2cScheduler for job scheduling. A2cScheduler consists of two agents, one of which, dubbed the actor, is responsible for learning the scheduling policy automatically and the other one, the critic, reduces the estimation error. Unlike previous policy gradient approaches, A2cScheduler is designed to reduce the gradient estimation variance and to update parameters efficiently. We show that the A2cScheduler can achieve competitive scheduling performance using both simulated workloads and real data collected from an academic data center.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Proceedings
Editors	Hady W. Lauw, Ee-Peng Lim, Raymond Chi-Wing Wong, Alexandros Ntoulas, See-Kiong Ng, Sinno Jialin Pan
Publisher	Springer
Pages	906-917
Number of pages	12
ISBN (Print)	9783030474355
DOIs	https://doi.org/10.1007/978-3-030-47436-2_68
State	Published - 2020
Event	24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020 - Singapore, Singapore Duration: May 11 2020 → May 14 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12085 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020
Country/Territory	Singapore
City	Singapore
Period	05/11/20 → 05/14/20

Keywords

Actor critic
Cluster scheduling
Deep reinforcement learning
Job scheduling

Access to Document

10.1007/978-3-030-47436-2_68

Cite this

Liang, S., Yang, Z., Jin, F., & Chen, Y. (2020). Data Centers Job Scheduling with Deep Reinforcement Learning. In H. W. Lauw, E-P. Lim, R. C-W. Wong, A. Ntoulas, S-K. Ng, & S. J. Pan (Eds.), Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Proceedings (pp. 906-917). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12085 LNAI). Springer. https://doi.org/10.1007/978-3-030-47436-2_68

Liang, Sisheng ; Yang, Zhou ; Jin, Fang et al. / Data Centers Job Scheduling with Deep Reinforcement Learning. Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Proceedings. editor / Hady W. Lauw ; Ee-Peng Lim ; Raymond Chi-Wing Wong ; Alexandros Ntoulas ; See-Kiong Ng ; Sinno Jialin Pan. Springer, 2020. pp. 906-917 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{772ee9968ca74ff6abb538c03c61ce46,

title = "Data Centers Job Scheduling with Deep Reinforcement Learning",

abstract = "Efficient job scheduling on data centers under heterogeneous complexity is crucial but challenging since it involves the allocation of multi-dimensional resources over time and space. To adapt the complex computing environment in data centers, we proposed an innovative Advantage Actor-Critic (A2C) deep reinforcement learning based approach called A2cScheduler for job scheduling. A2cScheduler consists of two agents, one of which, dubbed the actor, is responsible for learning the scheduling policy automatically and the other one, the critic, reduces the estimation error. Unlike previous policy gradient approaches, A2cScheduler is designed to reduce the gradient estimation variance and to update parameters efficiently. We show that the A2cScheduler can achieve competitive scheduling performance using both simulated workloads and real data collected from an academic data center.",

keywords = "Actor critic, Cluster scheduling, Deep reinforcement learning, Job scheduling",

author = "Sisheng Liang and Zhou Yang and Fang Jin and Yong Chen",

note = "Funding Information: Acknowledgement. We are thankful to the anonymous reviewers for their valuable feedback. This research is supported in part by the National Science Foundation under grant CCF-1718336 and CNS-1817094. Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; null ; Conference date: 11-05-2020 Through 14-05-2020",

year = "2020",

doi = "10.1007/978-3-030-47436-2_68",

language = "English",

isbn = "9783030474355",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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editor = "Lauw, {Hady W.} and Ee-Peng Lim and Wong, {Raymond Chi-Wing} and Alexandros Ntoulas and See-Kiong Ng and Pan, {Sinno Jialin}",

booktitle = "Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Proceedings",

}

Liang, S, Yang, Z, Jin, F & Chen, Y 2020, Data Centers Job Scheduling with Deep Reinforcement Learning. in HW Lauw, E-P Lim, RC-W Wong, A Ntoulas, S-K Ng & SJ Pan (eds), Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12085 LNAI, Springer, pp. 906-917, 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020, Singapore, Singapore, 05/11/20. https://doi.org/10.1007/978-3-030-47436-2_68

Data Centers Job Scheduling with Deep Reinforcement Learning. / Liang, Sisheng; Yang, Zhou; Jin, Fang et al.

Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Proceedings. ed. / Hady W. Lauw; Ee-Peng Lim; Raymond Chi-Wing Wong; Alexandros Ntoulas; See-Kiong Ng; Sinno Jialin Pan. Springer, 2020. p. 906-917 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12085 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Data Centers Job Scheduling with Deep Reinforcement Learning

AU - Liang, Sisheng

AU - Yang, Zhou

AU - Jin, Fang

AU - Chen, Yong

N1 - Funding Information: Acknowledgement. We are thankful to the anonymous reviewers for their valuable feedback. This research is supported in part by the National Science Foundation under grant CCF-1718336 and CNS-1817094. Publisher Copyright: © Springer Nature Switzerland AG 2020.

PY - 2020

Y1 - 2020

N2 - Efficient job scheduling on data centers under heterogeneous complexity is crucial but challenging since it involves the allocation of multi-dimensional resources over time and space. To adapt the complex computing environment in data centers, we proposed an innovative Advantage Actor-Critic (A2C) deep reinforcement learning based approach called A2cScheduler for job scheduling. A2cScheduler consists of two agents, one of which, dubbed the actor, is responsible for learning the scheduling policy automatically and the other one, the critic, reduces the estimation error. Unlike previous policy gradient approaches, A2cScheduler is designed to reduce the gradient estimation variance and to update parameters efficiently. We show that the A2cScheduler can achieve competitive scheduling performance using both simulated workloads and real data collected from an academic data center.

AB - Efficient job scheduling on data centers under heterogeneous complexity is crucial but challenging since it involves the allocation of multi-dimensional resources over time and space. To adapt the complex computing environment in data centers, we proposed an innovative Advantage Actor-Critic (A2C) deep reinforcement learning based approach called A2cScheduler for job scheduling. A2cScheduler consists of two agents, one of which, dubbed the actor, is responsible for learning the scheduling policy automatically and the other one, the critic, reduces the estimation error. Unlike previous policy gradient approaches, A2cScheduler is designed to reduce the gradient estimation variance and to update parameters efficiently. We show that the A2cScheduler can achieve competitive scheduling performance using both simulated workloads and real data collected from an academic data center.

KW - Actor critic

KW - Cluster scheduling

KW - Deep reinforcement learning

KW - Job scheduling

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U2 - 10.1007/978-3-030-47436-2_68

DO - 10.1007/978-3-030-47436-2_68

M3 - Conference contribution

AN - SCOPUS:85085740146

SN - 9783030474355

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 906

EP - 917

BT - Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Proceedings

A2 - Lauw, Hady W.

A2 - Lim, Ee-Peng

A2 - Wong, Raymond Chi-Wing

A2 - Ntoulas, Alexandros

A2 - Ng, See-Kiong

A2 - Pan, Sinno Jialin

PB - Springer

Y2 - 11 May 2020 through 14 May 2020

ER -

Liang S, Yang Z, Jin F, Chen Y. Data Centers Job Scheduling with Deep Reinforcement Learning. In Lauw HW, Lim E-P, Wong RC-W, Ntoulas A, Ng S-K, Pan SJ, editors, Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Proceedings. Springer. 2020. p. 906-917. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-47436-2_68

Data Centers Job Scheduling with Deep Reinforcement Learning

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