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Scaling laws and tradeoffs in peer-to-peer live multimedia streaming

机译:点对点实时多媒体流中的定律和权衡

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It is well-known that live multimedia streaming applications operate more efficiently when organized in peer-to-peer (P2P) topologies, since peer upload capacities are utilized to support other peers, and to alleviate the load and operating costs on the streaming servers. To date, there have been a number of existing experimental proposals with respect to how such peer-to-peer topologies are organized to support live streaming sessions. However, most of the existing proposals resort to intuition and heuristics when it comes to the design of such topology construction (i.e., neighbor selection) protocols. In this paper, we investigate the scaling laws of live P2P multimedia streaming, by quantitatively studying the asymptotic effects and tradeoffs among three key parameters in P2P streaming: server bandwidth cost, the maximum number of peers that can be supported, and the maximum number of streaming hops experienced by a peer. To further generalize our studies, we do not make restrictive assumptions in our theoretical analysis of such scaling laws: both peer upload capacities and peer lifetimes in a session may come from arbitrary distributions. With the theoretical insights we have developed, we propose Affinity, a simple and realistic heuristic to demonstrate the key benefits of our theoretical analysis in dynamic P2P networks, as compared to the topology construction algorithms in existing work.
机译:众所周知,当以对等(P2P)拓扑进行组织时,实时多媒体流应用程序将更有效地运行,这是因为利用对等上传功能来支持其他对等点,并减轻流服务器上的负载和运营成本。迄今为止,关于如何组织这种对等拓扑以支持实时流会话,已经有许多现有的实验性提议。然而,当涉及到这种拓扑构造(即,邻居选择)协议的设计时,大多数现有提议都求助于直觉和启发式。在本文中,我们通过定量研究P2P流中的三个关键参数之间的渐近效应和折衷,研究了实时P2P多媒体流的缩放定律:服务器带宽成本,可支持的对等体的最大数量以及最大的对等数量。对等体经历的流跃点。为了进一步概括我们的研究,我们在对此类缩放定律的理论分析中没有做出限制性假设:会话中的同级上载容量和同级生存期都可能来自任意分布。借助我们已经开发出的理论见解,我们提出了 Affinity (一种亲和力的启发式方法),以证明与现有工作中的拓扑构建算法相比,我们在动态P2P网络中进行理论分析的关键优势。

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