测量BitTorrent生态系统

Carmen Guerrero López
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引用次数: 0

摘要

只有摘要形式。BitTorrent是最成功的点对点应用。在过去的几年里,研究社区通过使用不同的测量技术从真实的BitTorrent群体中收集数据来研究BitTorrent生态系统。在这次演讲中,我们将介绍这些技术的第一次调查,这些技术构成了设计用于分析大规模系统的未来测量技术和工具的第一步。该技术可分为宏观、微观和互补。宏观技术允许收集种子的汇总信息,并呈现出非常高的可扩展性,能够在短时间内监控多达数十万个种子。更确切地说,微观技术在对等层上运行,并专注于理解性能方面,例如对等层的下载速率。它们提供了更高的粒度,但不像宏观技术那样可伸缩性好。最后,互补技术利用BitTorrent协议的最新扩展来获得聚合和对等层信息。该演讲还总结了研究界在准确测量BitTorrent生态系统方面所面临的主要挑战,如准确识别同行或估计同行的上传率。此外,我们提供了可能的解决方案来应对所描述的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Measuring BitTorrent ecosystems
Summary form only. 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 talk 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 to collect aggregated information of torrents and present a very high scalability being able to monitor up to hundreds of thousands of torrents in short periods of time. Rather, Microscopic techniques operate at the peer level and focus on understanding performance aspects such as the peers' download rates. They offer a higher granularity but do not scale as well as the Macroscopic techniques. Finally, Complementary techniques utilize recent extensions to the BitTorrent protocol in order to obtain both aggregated and peer level information. The talk also summarizes the main challenges faced by the research community to accurately measure the BitTorrent ecosystem such as accurately identifying peers or estimating peers' upload rates. Furthermore, we provide possible solutions to address the described challenges.
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