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.


    Access

    Download


    Export, share and cite



    Title :

    Feudal multiagent reinforcement learning for interdomain collaborative routing optimization


    Contributors:
    Li, Zhuo (author) / Zhou, Xu (author) / De Turck, Filip (author) / Li, Taixin (author) / Ren, Yongmao (author) / Qin, Yifang (author)

    Publication date :

    2022-01-01


    Remarks:

    WIRELESS COMMUNICATIONS & MOBILE COMPUTING ; ISSN: 1530-8669 ; ISSN: 1530-8677



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



    Class-based multicast routing in interdomain scenarios

    Nicolau, Maria João / Costa, António / Macedo, Joaquim et al. | BASE | 2008

    Free access

    Multiagent Reinforcement Learning and Game-Theoretic Optimization for Autonomous Sensor Control

    Ravier, Robert / Garagic, Denis / Galoppo, Travis et al. | IEEE | 2024


    A Social View to Multiagent Reinforcement Learning

    Wei, Q. / Sawaragi, T. | British Library Online Contents | 2004


    Autonomous Bus Fleet Control Using Multiagent Reinforcement Learning

    Sung-Jung Wang / S. K. Jason Chang | DOAJ | 2021

    Free access

    UAV Swarm Confrontation Using Hierarchical Multiagent Reinforcement Learning

    Baolai Wang / Shengang Li / Xianzhong Gao et al. | DOAJ | 2021

    Free access