A Hybrid Regression Model for Video Popularity-Based Cache Replacement in Content Delivery Networks

摘要

Content Delivery Networks (CDN) and their globally dispersed caches host a myriad of User Generated Videos (UGV) to meet end-user requests with quality of service. To efficiently utilize the limited storage of the caches, it is imperative to improve the hit ratio of UGVs. In contrast to the traditional static content, UGV popularity is highly dynamic and dependent on end-user behavior. Therefore, we devise a novel popularity prediction model for UGV, using a hybrid regression model. Our hybrid regression model dynamically adapts the popularity of UGV that is built from a historical training dataset. We reduce error in predicting popularity by up to 14%, when compared to pure offline and online approaches, with a small increase in the execution time and memory overhead. Our novel popularity prediction model accounts for end- user behavior by considering the end-user video watch time and the number of shares for the UGVs. To improve cache performance in CDN, we employ a cache replacement strategy that leverages our popularity prediction model to efficiently evict the less popular UGVs for more popular content. We compare our novel cache replacement strategy with the traditional and state-of-the-art cache replacement strategies and show an increase in the average hit ratio of up to 74% and 7%, respectively, for UGVs with shortterm popularity.