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. 16, 2020

Filed:

Dec. 19, 2018
Applicant:

Huawei Technologies Co., Ltd., Shenzhen, Guangdong, CN;

Inventors:

Trimponias Georgios, Hong Kong, CN;

Zhitang Chen, Shenzhen, CN;

Yanhui Geng, Montreal, CA;

Assignee:

HUAWEI TECHNOLOGIES CO., LTD., Shenzhen, Guangdong, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 15/16 (2006.01); H04L 12/24 (2006.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01); H04L 12/715 (2013.01); H04L 12/751 (2013.01); H04L 12/26 (2006.01); H04L 12/761 (2013.01);
U.S. Cl.
CPC ...
H04L 41/16 (2013.01); G06N 3/0445 (2013.01); G06N 3/0454 (2013.01); G06N 3/084 (2013.01); H04L 45/02 (2013.01); H04L 45/64 (2013.01); H04L 41/12 (2013.01); H04L 43/0882 (2013.01); H04L 45/16 (2013.01);
Abstract

Embodiments of this application provide a method for generating a routing control action in a software-defined network and a related device, to provide optimum control actions for the SDN. The method includes: obtaining a current network state parameter of the SDN; determining a Q function of the SDN based on the current network state parameter of the SDN and a deep neural network model, where the deep neural network model is determined based on a current topology structure of the SDN; and determining a routing control action for the SDN based on the Q function and a link state parameter of each link in the SDN. In the technical solution, the deep neural network model is combined with a Q-learning algorithm of reinforcement learning, and optimum control actions can be determined.


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