TY - GEN
T1 - Two-Stage Resource Scheduling for Deterministic Communication and Computation Integration
AU - Zhang, Weiting
AU - Tang, Nian
AU - Zhang, Chuan
AU - Guo, Ruibin
AU - Li, Mingyan
AU - Ying, Chenhao
AU - Jin, Jian
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - In this paper, we investigate a resource orchestration and transmission scheduling problem for data-intensive services with diversified service requirements. A three-layer collaborative architecture is presented to support dynamic networking and computing resource allocation. To obtain optimal orchestration and scheduling policies, we formulate a constrained resource scheduling problem with the objective to maximizing resource utilization and scheduling success ratio. Since the complicated coupled constraints among decisions, we decouple the problem into a two-stage sub-problems of resource orchestration and transmission scheduling. To realize cross-domain resource orchestration and deterministic transmission of large-scale computing tasks, a two-stage resource scheduling scheme is proposed. Specifically, the first stage makes the resource orchestration decision by a greedy algorithm, and the second stage makes the transmission scheduling decision based on a deep reinforcement learning algorithm. Simulation results show that the proposed solution can effectively improve resource utilization and scheduling success ratio while satisfying diversified service requirements, as compared with benchmarks.
AB - In this paper, we investigate a resource orchestration and transmission scheduling problem for data-intensive services with diversified service requirements. A three-layer collaborative architecture is presented to support dynamic networking and computing resource allocation. To obtain optimal orchestration and scheduling policies, we formulate a constrained resource scheduling problem with the objective to maximizing resource utilization and scheduling success ratio. Since the complicated coupled constraints among decisions, we decouple the problem into a two-stage sub-problems of resource orchestration and transmission scheduling. To realize cross-domain resource orchestration and deterministic transmission of large-scale computing tasks, a two-stage resource scheduling scheme is proposed. Specifically, the first stage makes the resource orchestration decision by a greedy algorithm, and the second stage makes the transmission scheduling decision based on a deep reinforcement learning algorithm. Simulation results show that the proposed solution can effectively improve resource utilization and scheduling success ratio while satisfying diversified service requirements, as compared with benchmarks.
UR - http://www.scopus.com/inward/record.url?scp=105000833446&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM52923.2024.10901540
DO - 10.1109/GLOBECOM52923.2024.10901540
M3 - Conference contribution
AN - SCOPUS:105000833446
T3 - Proceedings - IEEE Global Communications Conference, GLOBECOM
SP - 4179
EP - 4184
BT - GLOBECOM 2024 - 2024 IEEE Global Communications Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE Global Communications Conference, GLOBECOM 2024
Y2 - 8 December 2024 through 12 December 2024
ER -