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    网络传输公平性测量与算法设计:视频应用案例

    Fairness Measurement and Algorithm Design of Network Transmission: A Case Study of Video Applications

    • 摘要: 算网融合以计算为中心、网络为根基,通过网络连接异构计算节点,实现算网资源的高效分配与调度. 关于竞争流之间资源共享的公平性问题是算网融合的重要研究方向. 作为算网融合的典型场景,视频应用正变得越来越重要,但人们对于它们是否以及在多大程度上遵守公平性原则知之甚少. 在高度多样化的网络环境和缺乏自动化测量工具的情况下,公平性测量研究面临着巨大的挑战. 通过测量典型视频应用Zoom的竞争行为来研究这个问题发现,资源竞争行为是复杂多变的,Zoom在不同的场景下有着不同的资源抢占行为. 为了深入理解这些竞争行为,开发了自动化工具并进行测量以了解其用户体验(QoE)指标,包括端到端视频/音频时延、视频帧率和视频质量等. Zoom使用抢占带宽的策略来保证自身应用的用户体验. 为了追求更好的用户体验,Zoom往往会自私地发送过多的冗余数据包来应对异常的网络情况,其中一些是不必要的. 为此,设计一种能够在用户体验和公平性目标之间取得平衡的传输算法是非常重要的. 提出了算法QLibra,并通过实验证明它可以有效保障上层应用的用户体验并且对竞争流无害.

       

      Abstract: Computing-networking integration takes computing as the center and networking as the foundation, connects heterogeneous computing nodes through the network, and realizes the efficient allocation and scheduling of computing-networking resources. The fairness of resource sharing among competing flows is an important research direction of computing-networking integration. As typical scenarios, video applications are becoming more and more important, but little is known about whether and how much they adhere to the fairness principle. Given the highly diversified network environment and the shortage of automated measurement tools, fairness measurement study entails significant challenges. We investigate this problem by measuring the competing behaviors of typical video application (i.e., Zoom), and find that, resource competition behaviors are complex and transient, and Zoom has its own selfish behaviors in different operation scenarios. To take a deep dive into these competitive behaviors, we develop automated tools and conduct measurement to understand its QoE (quality of experience), including end-to-end video/voice delay, video frame rate, and video quality. We discover that the strategies of seizing bandwidth are used by Zoom to ensure its own QoE. In the pursuit of better QoE, Zoom tends to selfishly send excessively redundant packets to cope with abnormal network conditions, some of which are not necessary. To this end, it is important to specify a transport algorithm which is able to balance between QoE and fairness goals. We then present the design of QLibra, and demonstrate that it can effectively ensure the QoE and behave harmlessly to competing flows.

       

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