ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2021, Vol. 58 ›› Issue (6): 1275-1290.doi: 10.7544/issn1000-1239.2021.20201076

所属专题: 2021云网融合专题

• 网络技术 • 上一篇    下一篇



  1. 1(深圳大学计算机与软件学院 广东深圳 518060);2(上海市高可信计算重点实验室(华东师范大学) 上海 200062) (
  • 出版日期: 2021-06-01
  • 基金资助: 

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).

摘要: 随着智能终端设备的爆发式增长,多接入边缘计算(multi-access edge computing, MEC)成为支持多服务、多租户生态系统的关键技术之一.多接入边缘计算通过结合云端的移动计算技术和接入网的无线通信技术,实现了云端和网络的高效融合.然而,目前的边缘计算技术对于所有可能的资源(例如计算、通信、缓存)并没有细粒度的控制能力,因此并不能对延迟敏感的实时服务提供很好的支持.为了解决这个关键问题,设计了一种基于软件定义(software defined)的细粒度多接入边缘计算架构,可以对网络资源和计算资源进行细粒度的控制并进行协同管理,并设计了一种基于深度强化学习Q-Learning的两级资源分配策略,从而提供更有效的计算卸载和服务增强.大量的仿真实验证明了该架构的有效性.

关键词: 云网融合, 多接入边缘计算, 细粒度接入网, 软件定义网络, 深度强化学习

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