The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 25, 2024

Filed:

Aug. 23, 2023
Applicant:

Zhejiang Lab, Zhejiang, CN;

Inventors:

Yuan Liang, Hangzhou, CN;

Geyang Xiao, Hangzhou, CN;

Yuanhao He, Hangzhou, CN;

Tao Zou, Hangzhou, CN;

Ruyun Zhang, Hangzhou, CN;

Xiaofeng Cheng, Hangzhou, CN;

Assignee:

ZHEJIANG LAB, Hangzhou, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04L 47/12 (2022.01); G06N 20/00 (2019.01); H04L 41/16 (2022.01);
U.S. Cl.
CPC ...
H04L 47/12 (2013.01); G06N 20/00 (2019.01); H04L 41/16 (2013.01);
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.


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