Name
Abstract | ||
|---|---|---|
The past two years have witnessed an explosion of a new generation of livecast services, represented by Twitch.tv, GamingLive, and Dailymotion, to name but a few. With such a livecast service, geo-distributed Internet users can broadcast any event in real-time, for example, game, cooking, drawing, and so on, to viewers of interest. Its crowdsourced nature enables rich interactions among broadcasters and viewers but also introduces great challenges to accommodate their great scales and dynamics. To fulfill the demands from a large number of heterogeneous broadcasters and geo-distributed viewers, expensive server clusters have been deployed to ingest and transcode live streams. Yet our Twitch-based measurement shows that a significant portion of the unpopular and dynamic broadcasters are consuming considerable system resources; in particular, 25% of bandwidth resources and 30% of computational capacity are used by the broadcasters who do not have any viewers at all. In this article, through the real-world measurement and data analysis, we show that the public cloud has great potentials to address these scalability challenges. We accordingly present the design of Cloud-assisted Crowdsourced Livecast (CACL) and propose a comprehensive set of solutions for broadcaster partitioning. Our trace-driven evaluations show that our CACL design can smartly assign ingesting and transcoding tasks to the elastic cloud virtual machines, providing flexible and cost-effective system deployment. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3095755 | TOMCCAP |
Keywords | DocType | Volume |
Crowdsourced livecast,public clouds,workload migration,resource allocation | Journal | 13 |
Issue | ISSN | Citations |
3s | 1551-6857 | 0 |
PageRank | References | Authors |
0.34 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cong Zhang | 1 | 23 | 3.44 |
| Jiangchuan Liu | 2 | 4340 | 310.86 |
| Haiyang Wang | 3 | 573 | 73.18 |