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Multi-Agent Deep Q-Network Based Dynamic Controller Placement for Node Variable Software-Defined Mobile Edge-Cloud Computing Networks

Author

Listed:
  • Chenglin Xu

    (College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China)

  • Cheng Xu

    (College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China)

  • Bo Li

    (School of Computer Science and Engineering, Central South University, Changsha 410083, China)

Abstract

Software-defined networks (SDN) can use the control plane to manage heterogeneous devices efficiently, improve network resource utilization, and optimize Mobile Edge-Cloud Computing Networks (MECCN) network performance through decisions based on global information. However, network traffic in MECCNs can change over time and affect the performance of the SDN control plane. Moreover, the MECCN network may need to temporarily add network access points when the network load is excessive, and it is difficult for the control plane to form effective management of temporary nodes. This paper investigates the dynamic controller placement problem (CPP) in SDN-enabled Mobile Edge-Cloud Computing Networks (SD-MECCN) to enable the control plane to continuously and efficiently serve the network under changing network load and network access points. We consider the deployment of a two-layer structure with a control plane and construct the CPP based on this control plane. Subsequently, we solve this problem based on multi-agent DQN (MADQN), in which multiple agents cooperate to solve CPP and adjust the number of controllers according to the network load. The experimental results show that the proposed dynamic controller deployment algorithm based on MADQN for node-variable networks in this paper can achieve better performance in terms of delay, load difference, and control reliability than the Louvain-based algorithm, single-agent DQN-based algorithm, and MADQN- (without node-variable networks consideration) based algorithm.

Suggested Citation

  • Chenglin Xu & Cheng Xu & Bo Li, 2023. "Multi-Agent Deep Q-Network Based Dynamic Controller Placement for Node Variable Software-Defined Mobile Edge-Cloud Computing Networks," Mathematics, MDPI, vol. 11(5), pages 1-18, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:5:p:1247-:d:1087682
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    References listed on IDEAS

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    1. Chun-Ming Xu & Jia-Shuai Zhang & Ling-Qiang Kong & Xue-Bo Jin & Jian-Lei Kong & Yu-Ting Bai & Ting-Li Su & Hui-Jun Ma & Prasun Chakrabarti, 2022. "Prediction Model of Wastewater Pollutant Indicators Based on Combined Normalized Codec," Mathematics, MDPI, vol. 10(22), pages 1-15, November.
    2. Petra Csereoka & Bogdan-Ionuţ Roman & Mihai Victor Micea & Călin-Adrian Popa, 2022. "Novel Reinforcement Learning Research Platform for Role-Playing Games," Mathematics, MDPI, vol. 10(22), pages 1-12, November.
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