Evaluation of large-scale network systems and applications is usually done in one of three ways: simulations, real deployment on Internet, or on an emulated network testbed such as a cluster. Simulations can study very large systems but often abstract out many practical details, whereas real world tests are often quite small, on the order of a few hundred nodes at most, but have very realistic conditions. Clusters and other dedicated testbeds offer a middle ground between the two: large systems with real application code. They also typically allow configuring the testbed to enable repeatable experiments. In this paper we explore how to run large BitTorrent experiments in a cluster setup. We have chosen BitTorrent because the source code is available and it has been a popular target for research. Our contribution is twofold. First, we show how to tweak and configure the BitTorrent client to allow for a maximum number of clients to be run on a single machine, without running into any physical limits of the machine. Second, our results show that the behavior of BitTorrent can be very sensitive to the configuration and we revisit some existing BitTorrent research and consider the implications of our findings on previously published results. As we show in this paper, BitTorrent can change its behavior in subtle ways which are sometimes ignored in published works.
On the other hand, Rasti and Rejaie @cite_17 claim that the data obtained with this approach (injecting an instrumented client into real-world swarm) is not representative and has already been biased in the beginning. The main reason for their claim is that BitTorrent clients tend to cluster with other clients having similar upload bandwidths. This observation is definitely valid for measuring a real-world swarm on the Internet, but as our experiments are performed on a cluster where all peers are instrumented to provide logging information, such a bias does not exist in our experimental setup.
@cite_17
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The observed performance by individual peers in BitTorrent can be simply measured by their average download rate. While it is often stated that the observed peer-level performance by BitTorrent clients is high, it is difficult to accurately verify this claim due to the large scale, distributed and dynamic nature of this P2P system. To provide a "representative" characterization of peer-level performance in BitTorrent, the following two important questions should be addressed: (i) What is the distribution of observed performance among participating peers in a torrent? (ii) What are the primary peer-or group-level properties that determine observed performance by individual peers? In this paper, we conduct a measurement study to tackle these two questions. Toward this end, we derive observed performance for nearly all participating peers along with their main peer-and (peer-view of) group-level properties in three different torrents. Our results show that the probability of experiencing certain level of performance has a roughly uniform distribution across the entire range of observed values. Furthermore, while the performance of each peer has the highest correlation with its outgoing bandwidth, there is no dominant peer-and group-level property that primarily determines the observed performance by the majority of peers.