A deep reinforcement learning approach to multi-component job scheduling in edge computing

Zhi Cao; Honggang Zhang; Yu Cao; Benyuan Liu

doi:10.1109/MSN48538.2019.00018

A deep reinforcement learning approach to multi-component job scheduling in edge computing

Zhi Cao
, Honggang Zhang
, Yu Cao
, Benyuan Liu

Engineering

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

Original language	English
Title of host publication	Proceedings - 2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	19-24
Number of pages	6
ISBN (Electronic)	9781728152127
DOIs	https://doi.org/10.1109/MSN48538.2019.00018
State	Published - Dec 2019
Event	15th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2019 - Shenzhen, China Duration: Dec 11 2019 → Dec 13 2019

Publication series

Name	Proceedings - 2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2019

Conference

Conference	15th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2019
Country/Territory	China
City	Shenzhen
Period	12/11/19 → 12/13/19

ASJC Scopus Subject Areas

Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition
Signal Processing
Control and Optimization
Artificial Intelligence

Keywords

Deep Reinforcement Learning
Edge Computing

Access to Document

10.1109/MSN48538.2019.00018

Cite this

Cao, Z., Zhang, H., Cao, Y., & Liu, B. (2019). A deep reinforcement learning approach to multi-component job scheduling in edge computing. In Proceedings - 2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2019 (pp. 19-24). Article 9066027 (Proceedings - 2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MSN48538.2019.00018

A deep reinforcement learning approach to multi-component job scheduling in edge computing. / Cao, Zhi; Zhang, Honggang; Cao, Yu et al.
Proceedings - 2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 19-24 9066027 (Proceedings - 2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2019).

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

Cao, Z, Zhang, H, Cao, Y & Liu, B 2019, A deep reinforcement learning approach to multi-component job scheduling in edge computing. in Proceedings - 2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2019., 9066027, Proceedings - 2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2019, Institute of Electrical and Electronics Engineers Inc., pp. 19-24, 15th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2019, Shenzhen, China, 12/11/19. https://doi.org/10.1109/MSN48538.2019.00018

Cao Z, Zhang H, Cao Y, Liu B. A deep reinforcement learning approach to multi-component job scheduling in edge computing. In Proceedings - 2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 19-24. 9066027. (Proceedings - 2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2019). doi: 10.1109/MSN48538.2019.00018

Cao, Zhi ; Zhang, Honggang ; Cao, Yu et al. / A deep reinforcement learning approach to multi-component job scheduling in edge computing. Proceedings - 2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 19-24 (Proceedings - 2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2019).

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title = "A deep reinforcement learning approach to multi-component job scheduling in edge computing",

keywords = "Deep Reinforcement Learning, Edge Computing",

author = "Zhi Cao and Honggang Zhang and Yu Cao and Benyuan Liu",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 15th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2019 ; Conference date: 11-12-2019 Through 13-12-2019",

year = "2019",

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language = "English",

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AU - Cao, Zhi

AU - Zhang, Honggang

AU - Cao, Yu

AU - Liu, Benyuan

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Y1 - 2019/12

KW - Deep Reinforcement Learning

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M3 - Conference contribution

AN - SCOPUS:85084289532

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EP - 24

BT - Proceedings - 2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 15th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2019

Y2 - 11 December 2019 through 13 December 2019

ER -

A deep reinforcement learning approach to multi-component job scheduling in edge computing

Publication series

Conference

ASJC Scopus Subject Areas

Keywords

Access to Document

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