A Novel Reinforcement Learning Routing Algorithm for Congestion Control in Complex Networks

• URL: http://arxiv.org/abs/2401.00297v1
• Date: Sat, 30 Dec 2023 18:21:13 GMT
• Title: A Novel Reinforcement Learning Routing Algorithm for Congestion Control in Complex Networks
• Authors: Seyed Hassan Yajadda, Farshad Safaei
• Abstract summary: 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. Notably, the proposed method proves effective not only in Barab'asi-Albert scale-free networks but also in other network models such as Watts-Strogatz (small-world) and Erd"os-R'enyi (random network)
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 Barab\'asi-Albert scale-free networks but also in other network models such as Watts-Strogatz (small-world) and Erd\"os-R\'enyi (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.

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