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Adaptive Edge Caching based on Popularity and Prediction for Mobile Networks

Edge caching in mobile networks can improve users’ experience, reduce latency and balance the network traffic load. However, edge caching requires suitable strategies for determining what files to pre-fetch at which cell and at what time. Due to the heterogeneity of users’ content preferences and mobility, caching based only on popularity has limitations. Considering that cells located in different places have different predictability, in this paper, we propose an adaptive edge caching algorithm based on content popularity as well as the individual’s prediction results to provide an optimal caching strategy, aiming to maximize the cache hit rate with acceptable file replacement cost. A heuristic optimization strategy based on genetic algorithms is presented, along with a prediction model based on an improved Markov model for each user according to the historical data. In the model, similar users are clustered based on their behavior patterns. We evaluate our algorithm on a simulation dataset as well as a 3-week real-life dataset from China Mobile. The results show that our optimal caching strategy can improve the cache hit rate compared with other methods, especially when the storage capacity is small and the similarity in content requests of users is low.