Int J Performability Eng ›› 2022, Vol. 18 ›› Issue (5): 350-358.doi: 10.23940/ijpe.22.05.p5.350358

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Service Caching Strategy based on Edge Computing and Reinforcement Learning

Chengjie Xua, Dongcheng Lib, W. Eric Wongb, and Man Zhaoa,*   

  1. aSchool of Computer Science, China University of Geosciences, Wuhan, 430074, China;
    bDepartment of Computer Science, University of Texas at Dallas, 75082, USA
  • Submitted on ; Revised on ; Accepted on
  • Contact: * E-mail address:

Abstract: With the rapid development of the Internet of Things in recent years, there has been a dramatic increase in terminal units and new computationally and data-demanding applications. A terminal unit uploads data to the cloud server, which will be transmitted back to the terminal unit after certain operations. However, such a traditional cloud service is troubled by growing latency. Mobile edge computing emerges in such an environment. A short distance between the edge network and end-users mitigates this problem. However, the edge network has finite resources, making it impossible to deliver all service caching requests. To this end, a strategy is required to selectively cache services on the edge cloud. This study simulates the selection of edge services with a multi-armed bandit model and conducts a comparative study to analyze the impact that different algorithms have on performance.

Key words: edge computing, service caching, reinforcement learning, multi-armed bandit