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
N1 - Publisher Copyright:
© 2023 Owner/Author.
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
UR - http://www.scopus.com/inward/record.url?scp=85174258553&partnerID=8YFLogxK
U2 - 10.1145/3603165.3607396
DO - 10.1145/3603165.3607396
M3 - Conference contribution
AN - SCOPUS:85174258553
T3 - Proceedings of ACM Turing Award Celebration Conference, CHINA 2023
SP - 59
EP - 60
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 -