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Active measurements on BitTorrent and eMule ecosystem over the Internet

机译:通过互联网对BitTorrent和eMule生态系统进行主动测量

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Peer-to-peer applications have generated most volume of Internet traffic, which putting unprecedented pressure on ISPs. Besides simple throttling the traffic, some proposals suggested to optimize the overlay construction using locality-aware mechanism. Thus, detecting the individual peers, analyzing their distribution becomes a challenging task, which has not received the deserved attention. In this paper, we demonstrate an active measurement methodology to continuously trace the real-world BitTorrent and eMule/eDonkey peers over the Internet from a stub network and for a long period of time. Our measurements achieve the ability of real-time scanning the online peers, avoiding the potential copyright infringement and heavy traffic on Internet. During the over eight-month measurements, from July 15 2010 to March 31 2011, we crawled 33,008 torrents for BitTorrent and 19,840 meta-info for eMule/eDonkey, collected 8,963,776 and 1,538,408 distinct peers separately. And we thoroughly analyzed some distributions and preferences of peers, such as the geographical position, organization, type of IP address and the most used port, etc.
机译:对等应用程序产生了最多的Internet流量,这给ISP带来了空前的压力。除了简单地限制流量外,一些建议还建议使用位置感知机制来优化覆盖结构。因此,检测单个对等体,分析其分布成为一项具有挑战性的任务,尚未引起应有的重视。在本文中,我们演示了一种主动的测量方法,该方法可通过存根网络在很长一段时间内连续跟踪Internet上的真实BitTorrent和eMule / eDonkey对等端。通过我们的测量,可以实时扫描在线对等节点,从而避免了潜在的版权侵害和Internet上的繁忙流量。在2010年7月15日至2011年3月31日这8个月的评估中,我们针对BitTorrent抓取了33,008个种子,针对eMule / eDonkey抓取了19,840个元信息,分别收集了8,963,776和1,538,408个不同的对等节点。并且我们彻底分析了对等体的一些分布和偏好,例如地理位置,组织,IP地址的类型和最常使用的端口等。

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