A Long -Short-Term Fusion Approach for Video Cache

Owing to the unprecedented growth of video demands, video caching has been a basic network functionality in today's network architectures to offload backbone traffics, as well as provide users with lower access delay. Although abundant cache replacement algorithms have been proposed recently, they all suffer from a critical limitation: due to their immature rules, inaccurate feature engineering or unresponsive model update, they cannot strike a balance between the long-term history and short-term sudden events. To tackle this problem, we propose LA-E2, a long-short-term fusion cache replacement approach, which is based on a learning-aided exploration-exploitation process. Specifically, by effectively combining the deep neural network (DNN) based prediction with the online exploitation exploration through a top-k method, LA-E2 can both make use of the historical information and adapt to the constantly changing popularity responsively. Through the extensive experiments in two real-world datasets, LA-E2 is demonstrated to achieve state-of-the-art performance and generalize well. Especially when the cache size is small, LA-E2 outperforms the baselines by 17.5% 68.7% higher in total hit rate.