Sampling Bias in BitTorrent Measurements

Zhang, Boxun; Iosup, Alexandru; Pouwelse, Johan; Epema, Dick; Sips, Henk

doi:10.1007/978-3-642-15277-1_46

Cited by 15 publications

(14 citation statements)

References 25 publications

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“…A number of studies have focused on measuring and characterising specific properties of BitTorrent (e.g., [5,7]); other work has introduced improvements to the measuring process itself (e.g., [24,26,27]). The limitations of the evidence collected through indirect monitoring for legal cases motivated Bauer et al [1] to design BitStalker, an active probing mechanism for identifying hosts using BitTorrent to download files.…”

Section: Related Workmentioning

confidence: 99%

The Unbearable Lightness of Monitoring: Direct Monitoring in BitTorrent

Chothia

Cova

Novakovic

et al. 2013

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. It is known that BitTorrent file-sharing traffic is analysed to identify exchangers of copyrighted material. In general, copyright holders can perform monitoring using two approaches: indirect monitoring, where indirect clues of the sharing activity of a peer are considered (e.g., its presence in the peer list of a tracker), and direct monitoring, which establishes connections with peers to estimate their participation in sharing activity. Previous research has focused exclusively on indirect monitoring. We provide a broader characterisation of the monitoring of BitTorrent activity by considering both indirect and direct monitoring. In particular, we review previous work on indirect monitoring, provide features to detect peers engaged in such monitoring, and apply them to identify a number of monitoring organisations. Additionally, we introduce features that detect direct monitors, and provide the first ever measurements of direct monitoring, showing that it is now occurring.

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Section: Related Workmentioning

confidence: 99%

The Unbearable Lightness of Monitoring: Direct Monitoring in BitTorrent

Chothia

Cova

Novakovic

et al. 2013

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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“…Moreover, log traces are often problematic to obtain as they require agreement from content providers. The second group uses crawling (or spidering) techniques [8], [9], [16], [18], [20]. In order to obtain a comprehensive picture of the entire set of BitTorrent swarms, a crawler periodically contacts each tracker to obtain all the peer addresses that have been tracked.…”

Section: Tracker-level Measurementsmentioning

confidence: 99%

“…However, each swarm includes many unreachable peers (NATed, firewalled, or inactive at the time of the measurement). Owing to their inability to connect the crawler to unreachable peers, conventional peer-level measurement studies can measure only a part of all the peers in the swarm [9]. Second, the peer-level measurement method may degrade the performance of BitTorrent systems.…”

Section: Introductionmentioning

confidence: 99%

Deep Inspection of Unreachable BitTorrent Swarms

Yoshida

Nakao

2013

IEICE Trans. Inf. & Syst.

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Masahiro YOSHIDA †a) , Nonmember and Akihiro NAKAO †b) , Member SUMMARYBitTorrent is one of the most popular P2P file sharing applications worldwide. Each BitTorrent network is called a swarm, and millions of peers may join multiple swarms. However, there are many unreachable peers (NATed (network address translated), firewalled, or inactive at the time of measurement) in each swarm; hence, existing techniques can only measure a part of all the peers in a swarm. In this paper, we propose an improved measurement method for BitTorrent swarms that include many unreachable peers. In essence, NATed peers and those behind firewalls are found by allowing them to connect to our crawlers by actively advertising our crawlers' addresses. Evaluation results show that the proposed method increases the number of unique contacted peers by 112% compared to the conventional method. Moreover, the proposed method increases the total volume of downloaded pieces by 66%. We investigate the sampling bias among the proposed and conventional methods, and we find that different measurement methods yield significantly different results.

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“…Another reason is the difficulty of validating BitTorrent models using synthetic data or (only) small datasets-the complexity and heterogeneity of BitTorrent increase [30] the risk of biased models and over-fitting. Since OpenBitTorrent and PublicBitTorrent are nowadays two of the most populated public trackers, we assume that the results derived from the datasets of these trackers are representative for public BitTorrent trackers.…”

Section: B Datasetsmentioning

confidence: 99%

Identifying, analyzing, and modeling flashcrowds in BitTorrent

Zhang

Iosup

Pouwelse

et al. 2011

2011 IEEE International Conference on Peer-to-Peer Computing

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Abstract-Flashcrowds-sudden surges of user arrivals-do occur in BitTorrent, and they can lead to severe service deprivation. However, very little is known about their occurrence patterns and their characteristics in real-world deployments, and many basic questions about BitTorrent flashcrowds, such as How often do they occur? and How long do they last?, remain unanswered. In this paper, we address these questions by studying three datasets that cover millions of swarms from two of the largest BitTorrent trackers. We first propose a model for BitTorrent flashcrowds and a procedure for identifying, analyzing, and modeling BitTorrent flashcrowds. Then we evaluate quantitatively the impact of flashcrowds on BitTorrent users, and we develop an algorithm that identifies BitTorrent flashcrowds. Finally, we study statistically the properties of BitTorrent flashcrowds identified from our datasets, such as their arrival time, duration, and magnitude, and we investigate the relationship between flashcrowds and swarm growth, and the arrival rate of flashcrowds in BitTorrent trackers. In particular, we find that BitTorrent flashcrowds only occur in very small fractions (0.3-2%) of the swarms but that they can affect over ten million users.

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Sampling Bias in BitTorrent Measurements

Cited by 15 publications

References 25 publications

The Unbearable Lightness of Monitoring: Direct Monitoring in BitTorrent

The Unbearable Lightness of Monitoring: Direct Monitoring in BitTorrent

Deep Inspection of Unreachable BitTorrent Swarms

Identifying, analyzing, and modeling flashcrowds in BitTorrent

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