A3C-R: A QoS-Oriented Energy-Saving Routing Algorithm for Software-Defined Networks

• Published in: Future internet Vol. 17; no. 4; p. 158
• Main Authors: Wang, Sunan, Song, Rong, Zheng, Xiangyu, Huang, Wanwei, Liu, Hongchang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 03.04.2025
• Subjects: A3C; Algorithms; Artificial intelligence; Bandwidths; Communications traffic; Deep learning; Delay; Design; Efficiency; Energy consumption; energy saving; Indicators; Machine learning; Multiagent systems; Network topologies; Optimization; QoS; Quality of service; Quality of service architectures; Resource allocation; Routing (telecommunications); routing strategy; Scheduling; SDN; Software
• ISSN: 1999-5903; 1999-5903
• DOI: 10.3390/fi17040158

With the rapid growth of Internet applications and network traffic, existing routing algorithms are usually difficult to guarantee the quality of service (QoS) indicators such as delay, bandwidth, and packet loss rate as well as network energy consumption for various data flows with business characteristics. They have problems such as unbalanced traffic scheduling and unreasonable network resource allocation. Aiming at the above problems, this paper proposes a QoS-oriented energy-saving routing algorithm A3C-R in the software-defined network (SDN) environment. Based on the asynchronous update advantages of the asynchronous advantage Actor-Critic (A3C) algorithm and the advantages of independent interaction between multiple agents and the environment, the A3C-R algorithm can effectively improve the convergence of the routing algorithm. The process of the A3C-R algorithm first takes QoS indicators such as delay, bandwidth, and packet loss rate and the network energy consumption of the link as input. Then, it creates multiple agents to start asynchronous training, through the continuous updating of Actors and Critics in each agent and periodically synchronizes the model parameters to the global model. After the algorithm training converges, it can output the link weights of the network topology to facilitate the calculation of intelligent routing strategies that meet QoS requirements and lower network energy consumption. The experimental results indicate that the A3C-R algorithm, compared to the baseline algorithms ECMP, I-DQN, and DDPG-EEFS, reduces delay by approximately 9.4%, increases throughput by approximately 7.0%, decreases the packet loss rate by approximately 9.5%, and improves energy-saving percentage by approximately 10.8%.