Abstract
In recent years there has been a rapid increase of short video traffic in 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 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 QoE in traditional CDN has been much improved, prior solutions become invalid in solving the above challenges. In this article, 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: the CoStore predictor, which solves the non-stationary and unpredictability of local access pattern, by analyzing the complex video correlations; and 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.
| Original language | English (US) |
|---|---|
| Article number | 9003620 |
| Pages (from-to) | 194-199 |
| Number of pages | 6 |
| Journal | IEEE Network |
| Volume | 34 |
| Issue number | 3 |
| DOIs | |
| State | Published - May 1 2020 |
Bibliographical note
Funding Information:AcknoWledgment The work of Yuchao Zhang was supported in part by the National Natural Science Foundation of China (NSFC) Youth Science Foundation under Grant 61802024 and 61602051; the National Key R&D Program of China under Grant 2019YFB1802603; the Huawei Autonomous and Service 2.0 Project under Grant A2018185; and the CCF-Tencent Rhinoceros Creative Foundation under Grant IAGR20190103. The work of Pengmiao Li was supported in part by the BUPT Excellent Ph.D. Students Foundation under CX2019134. The work of Ke Xu was in part supported by the National Key R&D Program of China with no. 2018YFB0803405; the China National Funds for Distinguished Young Scientists with no. 61825204; and the Beijing Outstanding Young Scientist Program with no. BJJWZY-JH01201910003011.
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