DetCNCS: Deterministic Computing and Networking Convergence Scheduling

Weiting Zhang; Ruibin Guo; Dong Yang; Chuan Zhang

doi:10.1145/3603165.3607396

DetCNCS: Deterministic Computing and Networking Convergence Scheduling

Weiting Zhang, Ruibin Guo, Dong Yang, Chuan Zhang

School of Cyberspace Science and Technology

Beijing Jiaotong University

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

1 Citation (Scopus)

Abstract

In this article, we proposed a two-stage deep reinforcement learning (DRL) based deterministic scheduling architecture for computing and networking convergence, named as DetCNCS. By designing DRL algorithms for task offloading and global resource allocation, we achieved maximum utilizations of computing resources and deterministic end-To-end transmission with bounded latency.

Original language	English
Title of host publication	Proceedings of ACM Turing Award Celebration Conference, CHINA 2023
Publisher	Association for Computing Machinery, Inc
Pages	59-60
Number of pages	2
ISBN (Electronic)	9798400702334
DOIs	https://doi.org/10.1145/3603165.3607396
Publication status	Published - 28 Jul 2023
Event	2023 ACM Turing Award Celebration Conference, CHINA 2023 - Wuhan, China Duration: 28 Jul 2023 → 30 Jul 2023

Publication series

Name	Proceedings of ACM Turing Award Celebration Conference, CHINA 2023

Conference

Conference	2023 ACM Turing Award Celebration Conference, CHINA 2023
Country/Territory	China
City	Wuhan
Period	28/07/23 → 30/07/23

Keywords

computing and networking convergence
deep reinforcement learning
deterministic scheduling
task offloading

Access to Document

10.1145/3603165.3607396

Cite this

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title = "DetCNCS: Deterministic Computing and Networking Convergence Scheduling",

abstract = "In this article, we proposed a two-stage deep reinforcement learning (DRL) based deterministic scheduling architecture for computing and networking convergence, named as DetCNCS. By designing DRL algorithms for task offloading and global resource allocation, we achieved maximum utilizations of computing resources and deterministic end-To-end transmission with bounded latency.",

keywords = "computing and networking convergence, deep reinforcement learning, deterministic scheduling, task offloading",

author = "Weiting Zhang and Ruibin Guo and Dong Yang and Chuan Zhang",

note = "Publisher Copyright: {\textcopyright} 2023 Owner/Author.; 2023 ACM Turing Award Celebration Conference, CHINA 2023 ; Conference date: 28-07-2023 Through 30-07-2023",

year = "2023",

month = jul,

day = "28",

doi = "10.1145/3603165.3607396",

language = "English",

series = "Proceedings of ACM Turing Award Celebration Conference, CHINA 2023",

publisher = "Association for Computing Machinery, Inc",

pages = "59--60",

booktitle = "Proceedings of ACM Turing Award Celebration Conference, CHINA 2023",

}

Zhang, W, Guo, R, Yang, D & Zhang, C 2023, DetCNCS: Deterministic Computing and Networking Convergence Scheduling. in Proceedings of ACM Turing Award Celebration Conference, CHINA 2023. Proceedings of ACM Turing Award Celebration Conference, CHINA 2023, Association for Computing Machinery, Inc, pp. 59-60, 2023 ACM Turing Award Celebration Conference, CHINA 2023, Wuhan, China, 28/07/23. https://doi.org/10.1145/3603165.3607396

DetCNCS: Deterministic Computing and Networking Convergence Scheduling. / Zhang, Weiting; Guo, Ruibin; Yang, Dong et al.
Proceedings of ACM Turing Award Celebration Conference, CHINA 2023. Association for Computing Machinery, Inc, 2023. p. 59-60 (Proceedings of ACM Turing Award Celebration Conference, CHINA 2023).

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

TY - GEN

T1 - DetCNCS

T2 - 2023 ACM Turing Award Celebration Conference, CHINA 2023

AU - Zhang, Weiting

AU - Guo, Ruibin

AU - Yang, Dong

AU - Zhang, Chuan

PY - 2023/7/28

Y1 - 2023/7/28

N2 - In this article, we proposed a two-stage deep reinforcement learning (DRL) based deterministic scheduling architecture for computing and networking convergence, named as DetCNCS. By designing DRL algorithms for task offloading and global resource allocation, we achieved maximum utilizations of computing resources and deterministic end-To-end transmission with bounded latency.

AB - In this article, we proposed a two-stage deep reinforcement learning (DRL) based deterministic scheduling architecture for computing and networking convergence, named as DetCNCS. By designing DRL algorithms for task offloading and global resource allocation, we achieved maximum utilizations of computing resources and deterministic end-To-end transmission with bounded latency.

KW - computing and networking convergence

KW - deep reinforcement learning

KW - deterministic scheduling

KW - task offloading

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DO - 10.1145/3603165.3607396

M3 - Conference contribution

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T3 - Proceedings of ACM Turing Award Celebration Conference, CHINA 2023

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BT - Proceedings of ACM Turing Award Celebration Conference, CHINA 2023

PB - Association for Computing Machinery, Inc

Y2 - 28 July 2023 through 30 July 2023

ER -

DetCNCS: Deterministic Computing and Networking Convergence Scheduling

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