FastTrack：最小化基于CDN的视频流媒体系统的停滞时间
FastTrack: Minimizing Stalls for CDN-based Over-the-top Video Streaming Systems
AI总结：
本文提出FastTrack模型，通过优化CDN资源分配和存储带宽，减少视频流媒体系统的停滞时间尾概率，提升用户体验。
摘要：
互联网视频流媒体的流量迅速增长，并预计随着高清视频和物联网应用（如360度视频和增强现实应用）的普及而进一步增加。尽管高效管理异构云资源以优化用户体验至关重要，但现有研究往往忽略了重要因素。本文提出一个模型，描述当前视频流媒体应用的典型系统架构，通常由集中式源服务器和多个CDN站点组成。该模型综合考虑了CDN站点的有限缓存空间、视频请求的CDN分配、CDN不同端口的选择以及中心存储和带宽分配。基于该模型，我们专注于最小化性能指标——停滞时间尾概率（SDTP），并提出一种新颖且高效的算法来解决所提出的优化问题。还分析并展示了SDTP指标的理论界限。广泛的模拟结果表明，所提出的算法相比基线策略显著提高了SDTP指标。在真实云环境中的小型视频流媒体系统实现进一步验证了我们的结果。
英文摘要：
Traffic for internet video streaming has been rapidly increasing and is further expected to increase with the higher definition videos and IoT applications, such as 360 degree videos and augmented virtual reality applications. While efficient management of heterogeneous cloud resources to optimize the quality of experience is important, existing work in this problem space often left out important factors. In this paper, we present a model for describing a today's representative system architecture for video streaming applications, typically composed of a centralized origin server and several CDN sites. Our model comprehensively considers the following factors: limited caching spaces at the CDN sites, allocation of CDN for a video request, choice of different ports from the CDN, and the central storage and bandwidth allocation. With the model, we focus on minimizing a performance metric, stall duration tail probability (SDTP), and present a novel, yet efficient, algorithm to solve the formulated optimization problem. The theoretical bounds with respect to the SDTP metric are also analyzed and presented. Our extensive simulation results demonstrate that the proposed algorithms can significantly improve the SDTP metric, compared to the baseline strategies. Small-scale video streaming system implementation in a real cloud environment further validates our results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
