Feudal Multiagent Reinforcement Learning for Interdomain Collaborative Routing Optimization

• Published in: Wireless communications and mobile computing Vol. 2022; pp. 1 - 11
• Main Authors: Li, Zhuo, Zhou, Xu, De Turck, Filip, Li, Taixin, Ren, Yongmao, Qin, Yifang
• Format: Journal Article
• Language: English
• Published: Oxford Hindawi 27.03.2022; Hindawi Limited
• Subjects: Algorithms; Collaboration; Computer networks; Data transmission; Decision making; Decisions; Flexibility; Gateways; Internet; Machine learning; Markov processes; Multiagent systems; Network latency; Optimization; Parameter modification; Performance evaluation; Software-defined networking
• ISSN: 1530-8669; 1530-8677
• DOI: 10.1155/2022/1231979

In view of the inability of traditional interdomain routing schemes to meet the sudden network changes and adapt the routing policy accordingly, many optimization schemes such as modifying Border Gateway Protocol (BGP) parameters and using software-defined network (SDN) to optimize interdomain routing decisions have been proposed. However, with the change and increase of the demand for network data transmission, the high latency and flexibility of these mechanisms have become increasingly prominent. Recent researches have addressed these challenges through multiagent reinforcement learning (MARL), which can be capable of dynamically meeting interdomain requirements, and the multiagent Markov Decision Process (MDP) is introduced to construct this routing optimization problem. Thus, in this paper, an interdomain collaborative routing scheme is proposed in interdomain collaborative architecture. The proposed Feudal Multiagent Actor-Critic (FMAAC) algorithm is designed based on multiagent actor-critic and feudal reinforcement learning to solve this competition-cooperative problem. Our multiagent learns about the optimal interdomain routing decisions, focused on different optimization objectives such as end-to-end delay, throughput, and average delivery rate. Experiments were carried out in the interdomain testbed to verify the convergence and effectiveness of the FMAAC algorithm. Experimental results show that our approach can significantly improve various Quality of Service (QoS) indicators, containing reduced end-to-end delay, increased throughput, and guaranteed over 90% average delivery rate.