ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2021, Vol. 58 ›› Issue (6): 1275-1290.doi: 10.7544/issn1000-1239.2021.20201076

Special Issue: 2021云网融合专题

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A Fine-Grained Multi-Access Edge Computing Architecture for Cloud-Network Integration

Wang Lu1, Zhang Jianhao1, Wang Ting2, Wu Kaishun1   

  1. 1(College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060);2(Shanghai Key Laboratory of Trustworthy Computing (East China Normal University), Shanghai 200062)
  • Online:2021-06-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61872246, U2001207, 61872248), Guangdong Special Support Program, the Natural Science Foundation of Guangdong Province of China (2017A030312008), the Basic Research Program of Shenzhen (ZDSYS20190902092853047), the Science and Technology Innovative Program of Higher Education of Guangdong Province (2019KCXTD005), and the “Pearl River Talent Recruitment Program” of Guangdong Province (2019ZT08X603).

Abstract: Nowadays, a paradigm shift in mobile computing has been introduced by the ever-increasing heterogenous terminal devices, from the centralized mobile cloud towards the mobile edge. Multi-access edge computing (MEC) emerges as a promising ecosystem to support multi-service and multi-tenancy. It takes advantage of both mobile computing and wireless communication technologies for cloud-network integration. However, the physical hardware constraints of the terminal devices, along with the limited connection capacity of the wireless channel pose numerous challenges for cloud-network integration. The incapability of control over all the possible resources (e.g., computation, communication, cache) becomes the main hurdle of realizing delay-sensitive and real time services. To break this stalemate, this article investigates a software-defined fine-grained multi-access architecture, which takes full control of the computation and communication resources. We further investigate a Q-Learning based two-stage resource allocation strategy to better cater the heterogenous radio environments and various user requirements. We discuss the feasibility of the proposed architecture and demonstrate its effectiveness through extensive simulations.

Key words: cloud-network integration, multi-access edge computing (MEC), fine-grained access network, software-defined network, deep reinforcement learning

CLC Number: