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

机译:基于CDN的多层次缓存分析,基于CDN的顶部视频流系统

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摘要

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:Internet视频流量已迅速增加,并且进一步预计将随着新兴的5G应用程序增加,例如更高的定义视频,物联网和增强/虚拟现实应用程序。由于最终用户在大量的数量中消耗视频并且以越来越多的方式,应有效地管理内容分发网络(CDN)以提高系统效率。流服务可以包括多个缓存层,在分布式服务器和边缘路由器处,这些位置的高效内容管理会影响最终用户的经验质量(QoE)。在本文中,我们提出了一种用于视频流系统的模型,通常由集中原始服务器,几个CDN站点和靠近最终用户的边缘缓存组成。我们全面地考虑不同的系统设计因素,包括CDN站点的有限缓存空间,为视频请求分配CDN,从CDN和中央存储,带宽分配,边缘缓存容量选择不同的端口(或路径)和缓存政策。我们专注于最小化性能度量,失速持续时间尾概率(SDTP),并提出了一种用于多种设计灵活性的新颖和有效的算法。还分析和呈现了关于SDTP度量的理论界限。由OpenStack管理的虚拟化云系统的实现表明,与基线策略相比,所提出的算法可以显着提高SDTP度量。

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