首页|Service Caching Strategy based on Edge Computing and Reinforcement Learning
Service Caching Strategy based on Edge Computing and Reinforcement Learning
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
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.
NSTL
