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.
@cite_8 extend an earlier analytical model from @cite_0 and propose a new model for analytical investigation of BitTorrent's clustering. Their model only takes into account peer selection in BitTorrent and ignores the effects of piece selection. They observe similar clustering behavior as we have observed. However, their model and measurements exhibit a small discrepancy which they conjecture is the result of probabilistic effects from too small experiments. Our results show that clustering in BitTorrent is actually an interplay of both peer and piece selection algorithms, and we believe that their observed discrepancies are a result of their model ignoring piece selection. Although the effects of piece selection on clustering are small and hard to observe, our work, in particular on the download-constrained experiments, has shown that it cannot be ignored. Both @cite_8 and our work find the same effect of upload connections going to foreign peers while the majority of data comes from native peers.
@cite_0 @cite_8
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A number of analytical models exists that capture various properties of the BitTorrent protocol. However, until now virtually all of these models have been based on the assumption that the peers in the system have homogeneous bandwidths. As this is highly unrealistic in real swarms, these models have very limited applicability. Most of all, these models implicitly ignore BitTorrent's most important property: peer selection based on the highest rate of reciprocity. As a result, these models are not suitable for understanding or predicting the properties of real BitTorrent networks. Furthermore, they are hardly of use in the design of realistic BitTorrent simulators and new P2P protocols. In this paper, we extend existing work by presenting a model of a swarm in BitTorrent where peers have arbitrary upload and download bandwidths. In our model we group peers with (roughly) the same bandwidth in classes, and then analyze the allocation of upload slots from peers in one class to peers in another class. We show that our model accurately predicts the bandwidth clustering phenomenon observed experimentally in other work, and we analyze the resulting data distribution in swarms. We validate our model with experiments using real BitTorrent clients. Our model captures the effects of BitTorrent's well-known ‘tit-for-tat’ mechanism in bandwidth-inhomogeneous swarms and provides an accurate mathematical description of the resulting dynamics. In this paper, a simple mathematical model is presented for studying the performance of the BitTorrent (1) file sharing system. We are especially interested in the distribution of the peers with different states of the download job completedness. With the model we find that in the stable state the distribution of the download peers follows a U-shaped curve, and the parameters such as the departure rate of the seeds and the abort rate of the download peers will influence the peer distribution in different ways notably. We also analyze the file availability and the dying process of the BitTorrent file sharing system. We find that the system's stability deteriorates with the clustering of the peers, and BitTorrent's built-in "tit-for-tat" unchoking strategy could not help to preserve the integrity of the file among the download peers when the size of the community is small. An innovative peer selection strategy which enables more peers to finish the download job and prolongs the system's lifetime is proposed, in which the peers cooperate to improve the stability of the system by making a tradeoff between the current download rate and the future service availability. Finally, experimental results are presented to validate our analysis and findings. Existing studies on BitTorrent systems are single-torrent based, while more than 85 of all peers participate in multiple torrents according to our trace analysis. In addition, these studies are not sufficiently insightful and accurate even for single-torrent models, due to some unrealistic assumptions. Our analysis of representative Bit-Torrent traffic provides several new findings regarding the limitations of BitTorrent systems: (1) Due to the exponentially decreasing peer arrival rate in reality, service availability in such systems becomes poor quickly, after which it is difficult for the file to be located and downloaded. (2) Client performance in the BitTorrent-like systems is unstable, and fluctuates widely with the peer population. (3) Existing systems could provide unfair services to peers, where peers with high downloading speed tend to download more and upload less. In this paper, we study these limitations on torrent evolution in realistic environments. Motivated by the analysis and modeling results, we further build a graph based multi-torrent model to study inter-torrent collaboration. Our model quantitatively provides strong motivation for inter-torrent collaboration instead of directly stimulating seeds to stay longer. We also discuss a system design to show the feasibility of multi-torrent collaboration. 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.