  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.
