ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2021, Vol. 58 ›› Issue (6): 1318-1339.doi: 10.7544/issn1000-1239.2021.20201088

所属专题: 2021云网融合专题

• 网络技术 • 上一篇    下一篇



  1. (中国科学院计算技术研究所 北京 100190) (中国科学院大学 北京 100049) (
  • 出版日期: 2021-06-01
  • 基金资助: 

Online Joint Optimization Mechanism of Task Offloading and Service Caching for Multi-Edge Device Collaboration

Zhang Qiuping, Sun Sheng, Liu Min, Li Zhongcheng, Zhang Zengqi   

  1. (Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190) (University of Chinese Academy of Sciences, Beijing 100049)
  • Online: 2021-06-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61732017, 61872028, 62072436, 62002346).

摘要: 移动边缘计算通过在边缘设备上部署通信、计算、存储等资源,有效克服传统云计算存在的传输距离较长、响应时延过慢等问题,满足新兴的计算密集型和时延敏感型应用的服务需求.然而,移动边缘计算中存在边缘设备资源有限且多边缘设备间负载不均衡的问题.为了解决上述问题,多边缘设备协作成为一种必然趋势.然而,多边缘设备协作面临任务卸载与服务缓存相互耦合、边缘设备的任务负载及资源状态随时空双维变化等两大挑战,极大增加了求解难度.针对上述挑战,提出一种面向多边缘设备协作的任务卸载和服务缓存在线联合优化机制,将任务卸载和服务缓存联合优化问题解耦为服务缓存和任务卸载2个子问题.针对服务缓存子问题,提出基于情景感知组合多臂赌博机的协作服务缓存算法;针对任务卸载子问题,设计基于偏好的双边匹配算法.仿真实验表明所提算法能够有效降低任务整体执行时延,同时实现边缘设备间负载均衡.

关键词: 移动边缘计算, 任务卸载, 服务缓存, 协同计算, 联合优化

Abstract: By deploying communication, computing and storage resources on the edge devices, mobile edge computing (MEC) can effectively overcome the problems of long transmission distance and high response delay of traditional cloud computing. Therefore, MEC can satisfy the service requirements of emerging computation-intensive and delay-sensitive applications. Nevertheless, the resources of edge devices are limited and the workload among multiple edge devices is unbalanced in MEC. In order to address the above problems, multi-edge device collaboration becomes an inevitable trend. However, multi-edge device collaboration faces two challenges. First, task offloading and service caching are mutually coupled. Second, the workload and resource state of the edge devices have the characteristics of spatial-temporal change. The two challenges significantly increase the difficulty of solving this issue. In response to the above challenges, this paper proposes the online joint optimizing mechanism of task offloading and service caching for multi-edge device collaboration. And we decouple the joint optimizing problem into two sub-problems of service caching and task offloading in this paper. For the service caching sub-problem, a collaborative service caching algorithm based on contextual combinatorial multi-armed bandit is proposed. For the task offloading sub-problem, a preference-based double-side matching algorithm is designed. Simulation results demonstrate that the proposed algorithm in this paper can efficiently reduce the overall execution delay of tasks, and realize workload balancing among edge devices.

Key words: mobile edge computing, task offloading, service caching, collaborative computing, joint optimization