Abstract
Recent years have witnessed a rapid increase of short video traffic in content delivery network (CDN). While the video contributors change from large video studios to distributed ordinary end users, edge computing naturally matches the cache requirements from short video network. But the distributed edge caching exposes some unique characteristics: non-stationary user access pattern and temporal and spatial video popularity pattern, which severely challenge the edge caching performance. While the Quality of Experience (QoE) in traditional CDN has been much improved, prior solutions become invalid in solving the above challenges. In this chapter, we present AutoSight, a distributed edge caching system for short video network, which significantly boosts cache performance. AutoSight consists of two main components, solving the above two challenges, respectively: (i) the CoStore predictor, which solves the non-stationary and unpredictability of local access pattern, by analyzing the complex video correlations, and (ii) a caching engine Viewfinder, which solves the temporal and spatial video popularity problem by automatically adjusting future horizon according to video life span. All these inspirations and experiments are based on the real traces of more than 28 million videos with 100 million accesses from 488 servers located in 33 cities. Experiment results show that AutoSight brings significant boosts on distributed edge caching in short video network.
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