通过自动化和增强的日志记录进行BitTorrent群分析
BitTorrent Swarm Analysis through Automation and Enhanced Logging
摘要：
点对点协议目前是互联网中使用最多的协议类别，其中BitTorrent是最流行的内容分发协议。已经进行了大量的研究和调查，以衡量，分析和改进BitTorrent协议的内部工作。已经部署了跟踪器消息分析、网络探测和数据包嗅探等方法，以了解和增强BitTorrent的内部行为。在本文中，我们提出了一种新的方法，旨在收集，处理和分析大量的本地同行的BitTorrent群的信息。我们将信息分类为能够被实时的监控的周期性状态信息和用于后续分析的详细日志信息。我们设计并实现了一个检索、存储和呈现基础设施，可以轻松分析BitTorrent协议的内部结构。我们的方法既可以用作比较工具，也可以作为网络特性和协议实现如何影响整体BitTorrent群性能的测量系统。我们的方法基于一个框架，可以轻松地创建和控制不同的BitTorrent客户端。在虚拟化基础设施和客户端-服务器软件层的帮助下，我们能够创建，命令和管理大型BitTorrent群。该框架允许用户运行，调度，启动，停止群内的客户端，并收集有关其行为的信息。
英文摘要：
Peer-to-Peer protocols currently form the most heavily used protocol class in the Internet, with BitTorrent, the most popular protocol for content distribution, as its flagship. A high number of studies and investigations have been undertaken to measure, analyse and improve the inner workings of the BitTorrent protocol. Approaches such as tracker message analysis, network probing and packet sniffing have been deployed to understand and enhance BitTorrent's internal behaviour. In this paper we present a novel approach that aims to collect, process and analyse large amounts of local peer information in BitTorrent swarms. We classify the information as periodic status information able to be monitored in real time and as verbose logging information to be used for subsequent analysis. We have designed and implemented a retrieval, storage and presentation infrastructure that enables easy analysis of BitTorrent protocol internals. Our approach can be employed both as a comparison tool, as well as a measurement system of how network characteristics and protocol implementation influence the overall BitTorrent swarm performance. We base our approach on a framework that allows easy swarm creation and control for different BitTorrent clients. With the help of a virtualized infrastructure and a client-server software layer we are able to create, command and manage large sized BitTorrent swarms. The framework allows a user to run, schedule, start, stop clients within a swarm and collect information regarding their behavior.
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。
