Cost-efficient edge caching and Q-learning-based service selection policies in MEC

The paper focused on cache optimization and service selection algorithms in the cloud-edge environment. In order to solve the problem of cached content in edge servers, factors such as energy consumption and cost are considered, and finally a cache optimization model based on popularity was proposed. As for service selection, a Q-learning-based service selection algorithm is proposed to address the problems of dynamic task allocation and the optimization of Qos in the edge computing environment. The experimental results show that the proposed cache optimization and service selection algorithms in cloud-edge environment can better improve the cache hit ratio, minimize the transmission overhead, and ensure the server load balancing in the cloud-edge environment.

Automated summary

The paper focuses on cache optimization and service selection algorithms in the cloud-edge environment. A cache optimization model based on popularity is proposed to solve the problem of cached content in edge servers considering factors such as energy consumption and cost. For service selection, a Q-learning-based algorithm is proposed to address the dynamic task allocation and Qos optimization in the edge computing environment. Experimental results show that the proposed algorithms can significantly improve cache hit ratio, minimize transmission overhead, and ensure server load balancing in the cloud-edge environment.