LANC：用于更好的P2P流量定位的位置感知网络编码
LANC: locality-aware network coding for better P2P traffic localization
摘要：
随着ISP开始合作将其网络位置信息作为服务公开，例如，P4P，基于位置信息提供的P2P流量定位解决方案将很快接近其能力极限。一个自然的问题是：如果不能进一步改进局部信息，我们能做得更好吗？本文展示了如何利用本地信息可以限制传统的P2P数据调度算法，即使是复杂的本地最稀有的第一个政策。网络编码简化了P2P应用的数据调度，有利于提高P2P应用的吞吐量。本文提出了在拓扑结构构建和下载决策中同时使用局部性信息的局部性感知网络编码（LANC），并证明了其在P2P流量定位中的卓越能力。由网络编码引入的随机化增强了对等体在其邻域中找到创新块的机会。在适当的位置感知的帮助下，对等点从其邻近位置获得创新块的概率也将增加，从而更有效地利用网络资源。大量的仿真结果表明，LANC可以显着减少P2P流量冗余，而不牺牲应用级性能。在相同的局部性知识的帮助下，LANC的业务冗余在大多数情况下小于50%的目前最好的方法，不使用网络编码。
英文摘要：
As ISPs begin to cooperate to expose their network locality information as services, e.g., P4P, solutions based on locality information provision for P2P traffic localization will soon approach their capability limits. A natural question is: can we do any better provided that no further locality information improvement can be made? This paper shows how the utility of locality information could be limited by conventional P2P data scheduling algorithms, even as sophisticated as the local rarest first policy. Network coding's simplified data scheduling makes it competent for improving P2P application's throughput. Instead of only using locality information in the topology construction, this paper proposes the locality-aware network coding (LANC) that uses locality information in both the topology construction and downloading decision, and demonstrates its exceptional ability for P2P traffic localization. The randomization introduced by network coding enhances the chance for a peer to find innovative blocks in its neighborhood. Aided by proper locality-awareness, the probability for a peer to get innovative blocks from its proximity will increase as well, resulting in more efficient use of network resources. Extensive simulation results show that LANC can significantly reduce P2P traffic redundancy without sacrificing application-level performance. Aided by the same locality knowledge, the traffic redundancies of LANC in most cases are less than 50\% of the current best approach that does not use network coding.
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。
