Work

Faulty Links’ Fast Recovery Method Based on Deep Reinforcement Learning

Year: 2025

Type: article

Abstract: Aiming to address the high recovery delay and link congestion issues in the communication network of Wide-Area Measurement Systems (WAMSs), this paper introduces Software-Defined Networking (SDN) and proposes a deep reinforcement learning-based faulty-link fast recovery method (DDPG-LBBP). The DDPG-LBBP method takes delay and link utilization as the optimization objectives and uses gated recurrent neural network to accelerate algorithm convergence and output the optimal link weights for load balancing. By designing maximally disjoint backup paths, the method ensures the independence of the primary and backup paths, effectively preventing secondary failures caused by path overlap. The experiment compares the (1+2ε)-BPCA, FFRLI, and LIR methods using IEEE 30 and IEEE 57 benchmark power system communication network topologies. Experimental results show that DDPG-LBBP outperforms the others in faulty-link recovery delay, packet loss rate, and recovery success rate. Specifically, compared to the superior algorithm (1+2ε)-BPCA, recovery delay is decreased by about 12.26% and recovery success rate is improved by about 6.91%. Additionally, packet loss rate is decreased by about 15.31% compared to the superior FFRLI method.

Source: Algorithms

DOI: 10.3390/a18050241

Authors: Wanwei Huang, Wenqiang Gui, Yingying Li, Qingsong Lv, Jia Zhang, Xi He

Institutions: Zhengzhou University of Light Industry, Shenzhen Polytechnic University

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: gold

Estimated APC paid: $1,930