论文标题
PLEVER：用于边缘辅助实时视频交付的联合稳定分配和内容复制
PLVER: Joint Stable Allocation and Content Replication for Edge-assisted Live Video Delivery
论文作者
论文摘要
近年来，现场流媒体服务已获得极高的知名度。由于实时视频的尖锐流量模式，利用分布式边缘服务器来提高观众的体验质量（QOE）已成为如今的常见做法。然而，当前以客户端驱动的内容缓存机制不支持从云到边缘的缓存，从而导致实时视频交付中缺少大量缓存。最先进的研究通常会牺牲交付视频的失败，以解决上述问题。在本文中，通过共同考虑实时视频和边缘服务器的功能，我们提出了PLVer，这是一种主动的实时视频推动计划，以解决实时视频传递中的高速缓存问题。具体而言，PLVER首先在边缘群集和用户组之间进行一系列稳定的分配，以平衡边缘服务器上实时流量的负载。然后，它采用主动的视频复制算法来加快边缘服务器之间的视频复制。我们进行了广泛的痕量驱动评估，涵盖了300万个抽搐观众和300多个抽搐频道。结果表明，使用PLVER，Edge服务器分别比基于拍卖的复制方法和根据请求的时间方法进行缓存的流量多28％和82％。
The live streaming services have gained extreme popularity in recent years. Due to the spiky traffic patterns of live videos, utilizing the distributed edge servers to improve viewers' quality of experience (QoE) has become a common practice nowadays. Nevertheless, current client-driven content caching mechanism does not support caching beforehand from the cloud to the edge, resulting in considerable cache missing in live video delivery. State-of-the-art research generally sacrifices the liveness of delivered videos in order to deal with the above problem. In this paper, by jointly considering the features of live videos and edge servers, we propose PLVER, a proactive live video push scheme to resolve the cache miss problem in live video delivery. Specifically, PLVER first conducts a one-tomultiple stable allocation between edge clusters and user groups, to balance the load of live traffic over the edge servers. Then it adopts proactive video replication algorithms to speed up the video replication among the edge servers. We conduct extensive trace-driven evaluations, covering 0.3 million Twitch viewers and more than 300 Twitch channels. The results demonstrate that with PLVER, edge servers can carry 28% and 82% more traffic than the auction-based replication method and the caching on requested time method, respectively.
