An adaptive caching algorithm suitable for time-varying user accesses in VOD systems

Abstract

With the fast progresses of network technology, Video-On-Demand (VOD) service has found more and more applications. The transmission of multimedia files places heavy burdens on the Internet owing to their large sizes. To resolve this issue, caching servers are deployed at the edge of the Internet to meet most needs of local users by caching some popular videos. This paper provides an approach to choose the cached videos under the time-varying user behavior. Our approach estimates the average access intervals of a video with an Exponential Weighted Moving Average (EWMA) approach and furthermore predicts the video's future popularity based on its historical access intervals. The forgetting and predicting operations enable the algorithm to not only track the change of the time-varying user accesses, but also reduce the effects of the randomness of a single user access on the caching performance. In addition, we propose a new segmentation approach, which makes the storage granularity independent from the management granularity and can make a better use of the cache space. Simulation results show that our segmentation approach has a higher Byte-Hit Ratio than uniform segmentation and chunk segmentation, and our caching algorithm outperforms Least Recently Used (LRU), Least Frequently Used (LFU) and EWMA.