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Measuring the bittorrent ecosystem: Techniques, tips, and tricks

机译:评估bittorrent生态系统:技术,技巧和窍门

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BitTorrent is the most successful peer-to-peer application. In the last years the research community has studied the BitTorrent ecosystem by collecting data from real BitTorrent swarms using different measurement techniques. In this article we present the first survey of these techniques that constitutes a first step in the design of future measurement techniques and tools for analyzing large-scale systems. The techniques are classified into macroscopic, microscopic, and complementary. Macroscopic techniques allow us to collect aggregated information of torrents and present very high scalability, able to monitor up to hundreds of thousands of torrents in short periods of time. Microscopic techniques operate at the peer level and focus on understanding performance aspects such as the peers?? download rates. They offer higher granularity but do not scale as well as macroscopic techniques. Finally, complementary techniques utilize recent extensions to the BitTorrent protocol in order to obtain both aggregated and peer-level information. The article also summarizes the main challenges faced by the research community to accurately measure the BitTorrent ecosystem such as accurately identifying peers and estimating peers' upload rates. Furthermore, we provide possible solutions to address the described challenges.
机译:BitTorrent是最成功的对等应用程序。在过去的几年中,研究社区通过使用不同的测量技术从真实的BitTorrent群体收集数据来研究BitTorrent生态系统。在本文中,我们将对这些技术进行首次调查,这构成了设计未来测量技术和分析大型系统的工具的第一步。这些技术分为宏观,微观和互补。宏观技术使我们能够收集种子的汇总信息,并具有很高的可伸缩性,能够在短时间内监视多达数十万个种子。微观技术在对等层运行,并专注于理解性能方面,例如对等层?下载率。它们提供更高的粒度,但不像宏观技术那样可扩展。最后,补充技术利用了对BitTorrent协议的最新扩展,以获得聚合信息和对等层信息。本文还总结了研究社区在准确测量BitTorrent生态系统方面面临的主要挑战,例如准确识别对等体和估计对等体的上载率。此外,我们提供了可能的解决方案来解决上述挑战。

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