公开(公告)号：US20240129236A1

公开(公告)日：2024-04-18

申请号：US18454782

申请日：2023-08-23

Applicant: ZHEJIANG LAB

Inventor： Yuan LIANG ,  Geyang XIAO ,  Yuanhao HE ,  Tao ZOU ,  Ruyun ZHANG ,  Xiaofeng CHENG

IPC: H04L47/12 ,  G06N20/00 ,  H04L41/16

CPC classification number: H04L47/12 ,  G06N20/00 ,  H04L41/16

Abstract: The present application discloses a DQN-based distributed computing network coordinate flow scheduling system and method. The method includes: establishing environmental feature data based on distributed computing task information and a congestion situation of a port queue in a programmable forwarding platform on a data plane, establishing and training a deep reinforcement learning intelligent agent based on an action value network and a target network in DQN, and the deep reinforcement learning intelligent agent outputting abstract actions; receiving, by a policy mapper, the abstract actions and mapping them into an executable coordinate flow scheduling policy; executing, by the programmable forwarding platform, the executable coordinate flow scheduling policy and updating the congestion situation of the port queue; and recording, a policy gainer, a completion time of a distributed computing task as a real-time reward of the deep reinforcement learning intelligent agent and iteratively optimizing the deep reinforcement learning intelligent agent.