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Deep Reinforcement Learning based Optimal Routing with Software-Defined Networking

Year: 2024

Type: article

Abstract: An effective method for network congestion avoidance is to optimize the routing. We propose an optimal routing controlling that combines Deep Reinforcement learning(DRL) based policy optimization and Software Defined Network(SDN) based flow control. It uses DRL to learn the optimal network routing policy in different traffic patterns. SDN controller provides network traffic information for DRL agent to make decision, and implements flow routing control according to the decision. The results of simulation experiments show that our DRL-based route optimization has good performances in congestion avoidance.

Source: International Journal of Next-Generation Networks

DOI: 10.5121/ijngn.2024.16401

Authors: Yingjiang Liu, Jiaxin Zhang, Yiling Yang, Ran Yang, Yuehui Tan

Institutions: Beijing University of Posts and Telecommunications, China Mobile (China)

Language: English

Topic: Software-Defined Networks and 5G

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

OpenAlex ID: W4406462780

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