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Multi-Tier Caching Analysis in CDN-Based Over-the-Top Video Streaming Systems

机译:基于CDN的顶层视频流系统中的多层缓存分析

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Internet video traffic has been rapidly increasing and is further expected to increase with the emerging 5G applications, such as higher definition videos, the IoT, and augmented/virtual reality applications. As end users consume video in massive amounts and in an increasing number of ways, the content distribution network (CDN) should be efficiently managed to improve the system efficiency. The streaming service can include multiple caching tiers, at the distributed servers and the edge routers, and efficient content management at these locations affects the quality of experience (QoE) of the end users. In this paper, we propose a model for video streaming systems, typically composed of a centralized origin server, several CDN sites, and edge-caches located closer to the end user. We comprehensively consider different systems design factors, including the limited caching space at the CDN sites, allocation of CDN for a video request, choice of different ports (or paths) from the CDN and the central storage, bandwidth allocation, the edge-cache capacity, and the caching policy. We focus on minimizing a performance metric, stall duration tail probability (SDTP), and present a novel and efficient algorithm accounting for the multiple design flexibilities. The theoretical bounds with respect to the SDTP metric are also analyzed and presented. The implementation of a virtualized cloud system managed by Openstack demonstrates that the proposed algorithms can significantly improve the SDTP metric compared with the baseline strategies.
机译:互联网视频流量一直在快速增长,并且随着新兴的5G应用(例如高清视频,IoT和增强/虚拟现实应用)的增长,预计还会增长。随着最终用户大量且以越来越多的方式消费视频,应该有效地管理内容分发网络(CDN)以提高系统效率。流服务可以在分布式服务器和边缘路由器上包括多个缓存层,并且在这些位置的有效内容管理会影响最终用户的体验质量(QoE)。在本文中,我们提出了一种视频流系统的模型,该模型通常由集中式原始服务器,几个CDN站点和更靠近最终用户的边缘缓存组成。我们会综合考虑不同的系统设计因素,包括CDN站点上有限的缓存空间,为视频请求分配CDN,从CDN和中央存储中选择不同的端口(或路径),带宽分配,边缘缓存容量,以及缓存策略。我们专注于最小化性能指标,停顿持续时间尾部概率(SDTP),并提出了一种新颖高效的算法,考虑了多种设计灵活性。相对于SDTP度量的理论界限也进行了分析和介绍。由Openstack管理的虚拟化云系统的实施证明,与基线策略相比,所提出的算法可以显着改善SDTP指标。

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