DetCNCS: Deterministic Computing and Networking Convergence Scheduling
Weiting Zhang, Ruibin Guo, Dong Yang, Chuan Zhang · 2023
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.