Congrats to Xu and Zhengnan!
Title: Cooperative Edge Caching Based on Temporal Convolutional Network
Abstract: With the rapid growth of networked multimedia services in the Internet, wireless network traffic has increased dramatically. However, the current mainstream content caching scheme does not take into account the cooperation of different edge servers, resulting in deteriorated system performance. In this paper, we propose a learning-based edge caching scheme to enable mutual cooperation among different edge servers with limited caching resources, thus effectively reducing the content delivery latency. Specifically, we formulate the cooperative content caching problem as an optimization problem, which is proven to be NP-hard. To solve it, a learning-based cooperative caching strategy (LECS) is designed, which encompasses three steps. Firstly, a temporal convolutional network driven content popularity prediction model is designed to estimate the content popularity with high accuracy. Secondly, with the predicted content popularity, the concept of content caching value (CCV) is introduced to weigh the value of a content when cached on a given edge server. Thirdly, a dynamic programming algorithm is proposed to maximize the overall CCV. Real-traces driven simulations show the superiority of our approach. The experimental results show that LECS is superior to the state-of-the-art caching schemes in terms of the average content delivery delay and the cache hit rate.