The Journal of China Universities of Posts and Telecommunications ›› 2022, Vol. 29 ›› Issue (3): 54-68.doi: 10.19682/j.cnki.1005-8885.2021.1018

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Intelligent Service Function Chain Mapping Framework for Cloud-and-Edge-Collaborative IoT


  1. 1. Information and Communication Branch, State Grid Liaoning Electric Power Co. , Ltd. , Shenyang 110004, China
    2. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
    3. State Grid Liaoning Electric Power Research Institute, Shenyang 110004, China
  • Received:2021-03-01 Revised:2021-10-02 Online:2022-06-30 Published:2022-06-30
  • Contact: Xu Siya
  • Supported by:
    This work was supported by the Science and Technology Project of State Grid Corporation of China

Abstract: With the rapid development of Internet of thing (IoT) technology, it has become a challenge to deal with the increasing number and diverse requirements of IoT services. By combining burgeoning network function virtualization ( NFV) technology with cloud computing and mobile edge computing ( MEC), an NFV-enabled cloud-and-edge-collaborative IoT (CECIoT) architecture can efficiently provide flexible service for IoT traffic in the form of a service function chain (SFC) by jointly utilizing edge and cloud resources. In this promising architecture, a difficult issue is how to balance the consumption of resource and energy in SFC mapping. To overcome this challenge, an intelligent energy-and-resource-balanced SFC mapping scheme is designed in this paper. It takes the comprehensive deployment consumption as the optimization goal, and applies a deep Q-learning(DQL)-based SFC mapping (DQLBM) algorithm as well as an energy-based topology adjustment (EBTA) strategy to make efficient use of the limited network resources, while satisfying the delay requirement of users. Simulation results show that the proposed scheme can decrease service delay, as well as energy and resource consumption.

Key words: Internet of thing (IoT), mobile edge computing, network function virtualization (NFV), deep Q-learning (DQL)