A Novel Reinforcement Learning Routing Algorithm for Congestion Control in Complex Networks
This addresses congestion management in communication networks, which is an incremental improvement over existing routing methods.
The paper tackles congestion control in complex networks by developing a reinforcement learning routing algorithm that optimizes both congestion and path length, achieving up to 30% improvement in efficiency criteria and reducing maximum node congestion by five times across various network topologies.
Despite technological advancements, the significance of interdisciplinary subjects like complex networks has grown. Exploring communication within these networks is crucial, with traffic becoming a key concern due to the expanding population and increased need for connections. Congestion tends to originate in specific network areas but quickly proliferates throughout. Consequently, understanding the transition from a flow-free state to a congested state is vital. Numerous studies have delved into comprehending the emergence and control of congestion in complex networks, falling into three general categories: soft strategies, hard strategies, and resource allocation strategies. This article introduces a routing algorithm leveraging reinforcement learning to address two primary objectives: congestion control and optimizing path length based on the shortest path algorithm, ultimately enhancing network throughput compared to previous methods. Notably, the proposed method proves effective not only in Barabási-Albert scale-free networks but also in other network models such as Watts-Strogatz (small-world) and Erdös-Rényi (random network). Simulation experiment results demonstrate that, across various traffic scenarios and network topologies, the proposed method can enhance efficiency criteria by up to 30% while reducing maximum node congestion by five times.