A Novel Reinforcement Learning Routing Algorithm for Congestion Control in Complex Networks
Year: 2023
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Yajadda, Seyed Hassan, Safaei, Farshad
Institution: Shahid Beheshti University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4390528634