Clustering and sharing incentives in BitTorrent systems

Legout, Arnaud; Liogkas, Nikitas; Kohler, Eddie; Zhang, Lixia

doi:10.1145/1254882.1254919

Cited by 183 publications

(73 citation statements)

References 17 publications

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“…Legout et al [3] showed that the clustering produced by the choking algorithm favors leechers with higher upload bandwidth. The malicious leecher 3 perturbs the clustering produced by the choking algorithm.…”

Section: Attack Details and Experimental Evaluationmentioning

confidence: 99%

“…Good clustering ensures that the leechers with high upload bandwidth are rewarded with higher download rates and they are able to complete their downloads more quickly. Finally, the choking algorithm has been shown to converge towards good clustering shortly after the beginning of the download [3]. We refer interested readers to [1] for a detailed description of the choking algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Poster

Adamsky

Khan

Rajarajan

et al. 2011

Proceedings of the 18th ACM Conference on Computer and Communications Security

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BitTorrent protocol incentivizes sharing through its choking algorithm. BitTorrent choking algorithm creates clusters of leechers with similar upload capacity to achieve higher overall transfer rates. We show that a malicious peer can exploit BitTorrent's choking algorithm to reduce the upload utilization of high bandwidth leechers. We use a testbed comprising of 24 nodes to provide experimental evidence of a distributed attack in which the malicious peers increase the download time for high bandwidth leechers by up to 16 % and increases average download time of the swarm by up to 15 % by using distributed and loosely-coupled malicious peers which comprise only 4.7 % of the swarm. The countermeasures of this attack are a part of our ongoing research work.

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Section: Attack Details and Experimental Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Poster

Adamsky

Khan

Rajarajan

et al. 2011

Proceedings of the 18th ACM Conference on Computer and Communications Security

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“…Prima facie, the notion of coalition described above appears similar to that of clusters studied in [14], where a cluster is defined to be a set of peers with similar upload capacities. However, the clusters studied in [14] are formed as a consequence of the selfish nature of peers and of using the Tit-for-Tat strategy.…”

Section: Introductionmentioning

confidence: 99%

“…However, the clusters studied in [14] are formed as a consequence of the selfish nature of peers and of using the Tit-for-Tat strategy. Unlike a coalition, there is no explicit cooperation among peers in a cluster.…”

Section: Introductionmentioning

confidence: 99%

Coalitions Improve Performance in Data Swarming Systems

Zhang

Vasudevan

Li³

et al. 2015

IEEE/ACM Trans. Networking

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We present an argument in favor of forming coalitions of peers in a data swarming system consisting of peers with heterogeneous upload capacities. In this paper, a coalition refers to a set of peers that explicitly cooperate with other peers inside the coalition via choking, piece selection, and capacity allocation strategies. Furthermore, each peer in a coalition exchanges data with peers outside its coalition via distinct choking, piece selection, and capacity allocation strategies. We first propose a simple Random Choking strategy for peers inside a coalition and develop an analytical model for studying its performance. Our model accurately predicts a coalition's performance and shows that the proposed strategy helps a coalition achieve near-optimal performance. Furthermore, our model can be easily adapted to model a BitTorrent-like swarm. We show that our Random Choking strategy significantly outperforms Tit-for-Tat and Unchoke-All strategies proposed in prior work. We also introduce a simple piece selection strategy, which significantly improves data availability within a coalition as compared to Rarest-First strategy employed in BitTorrent systems. Using cooperative game theory, we prove the existence of stable coalitions when peer population is fixed and each peer has complete information of other peers' actions and payoffs. When peers are allowed to freely join or leave coalitions, we propose a Cooperation-Aware Better Response strategy that achieves convergence of the dynamic coalition formation process. Finally, using extensive simulations, we demonstrate that forming coalitions results in significant improvements in the overall performance of a data swarm.

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“…Other solutions have focused on improving the piece selection algorithm, at the seed (source of the content) [5]- [7] or for all peers [8] showing improvements with respect to, e.g., the classical BitTorrent protocol [9], either in static [8] or in dynamic overlays [6], [7]. An alternative line of work has focused instead on characterizing the swarming effects by measurement studies [10], [11], and by modeling the behavior of swarming protocols [12], [13]. These analytical results assume that peersets are static, and their analysis is at the steady state.…”

Section: Introductionmentioning

confidence: 99%

Integrating Piece and Peer Selection in Content Distribution Networks

Esposito¹,

Cerroni²

2014

2015 IEEE Global Communications Conference (GLOBECOM)

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Content Distribution Networks (CDNs) could benefit from peer-to-peer (P2P) network techniques to improve content delivery time by leveraging the upload capacity of the entire network. In most available solutions, peers first select a set of partners, and later select the pieces of content to exchange. It is fundamental instead that both peer and piece selection algorithms are performed in a consistent and integrated way.To this aim, we propose a content distribution protocol that unifies piece and peer selection in a single algorithm which optimizes the swarming effects. Our approach leverages Flajolet-Martin sketches, a technique based on Bloom filters, to estimate the number of distinct pieces in the CDN, and then adopts a fractional knapsack problem approach to effectively utilize the entire upload capacity of each peer. We tested our solution with both simulations and Planetlab experiments, showing how the piece estimation at every peer is a good approximation of the global rarest piece across the network, and not just across the first hop neighborhood. Moreover, we show how our solution improves the average downloading time by up to 20%. If we consider only the fastest 50% of peers, the downloading time is improved by 100%. Furthermore, our solution decreases the average first content uploading time by 80% with respect to standard P2P protocols which use a local rarest first piece selection, and titfor-tat as peer selection algorithm.

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Clustering and sharing incentives in BitTorrent systems

Cited by 183 publications

References 17 publications

Poster

Poster

Coalitions Improve Performance in Data Swarming Systems

Integrating Piece and Peer Selection in Content Distribution Networks

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