Tribler

Zeilemaker, Niels; Capotă, Mihai; Bakker, A.; Pouwelse, Johan

doi:10.1145/2072298.2072433

Cited by 13 publications

(1 citation statement)

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“…Tribler [5] is a P2P system that uses a decentralized form of accounting called BarterCast [6]. Tribler is backwards compatible with BitTorrent and uses many of the concepts introduced by the latter: content to be shared is divided in pieces that are cryptographically hashed; the hashes are collected in a torrent file which is used to uniquely identify the content; the group of peers sharing a torrent is a called a swarm; servers called trackers can be used by peers when joining a swarm to find other peers.…”

Section: Introductionmentioning

confidence: 99%

Decentralized credit mining in P2P systems

Capotă

Pouwelse

Epema

2015

2015 IFIP Networking Conference (IFIP Networking)

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Accounting mechanisms based on credit are used in peer-to-peer systems to track the contribution of peers to the community for the purpose of deterring freeriding and rewarding good behavior. Most often, peers earn credit for uploading files, but other activities might be rewarded in the future as well, such as making useful comments or reporting spam. Credit earned can be used for accessing new content, or for receiving preferential treatment in case of network congestion. We define credit mining as the activity performed by peers for the purpose of earning credit. In this paper, we design, implement, and evaluate a system for decentralized credit mining that maximizes the contribution of idle peers to the community by automatically uploading popular files. Building on previous theoretical insights into the economics of communities, we select autonomous algorithms for bandwidth investment as the basis of our credit mining system. Additionally, we describe our experience with important challenges arising from Internet deployment, that are frequently neglected in emulation, including duplicate content avoidance, spam prevention, and the cost of keeping peer information updated. Furthermore, we implement an archival mode of operation, which prevents the disappearance of old content from the community. We show the feasibility and usefulness of our credit mining system through measurements from our implementation on top of Tribler, an Internetdeployed peer-to-peer system.

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Section: Introductionmentioning

confidence: 99%

Decentralized credit mining in P2P systems

Capotă

Pouwelse

Epema

2015

2015 IFIP Networking Conference (IFIP Networking)

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Byzantine-resilient dual gossip membership management in clouds

et al. 2017

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Tracking freeriders in gossip-based content dissemination systems

et al. 2014

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a b s t r a c tGossip-based protocols have proven very efficient for disseminating high-bandwidth content such as video streams in a peer-to-peer fashion. However, for the protocols to work, nodes are required to collaborate by devoting a fraction of their upload bandwidth, a scarce resource for some of them, to forward the content they receive to other nodes. Consequently, such protocols suffer from freeriding, a common phenomenon on the Internet, which consists in selfishly benefiting from the system without contributing its fair share. Due to the dynamic nature and the inherent randomness of gossip protocols and to the high scalability requirements of video streaming systems, detecting freeriders is a difficult challenge.This paper presents LiFTinG, the first protocol for detecting freeriders, including colluding ones, in gossip-based content dissemination systems with asymmetric data exchanges. In addition, LiFTinG is still able to detect freeriders when network coding, a widely used technique to improve the efficiency of content dissemination, is used. LiFTinG relies on nodes to track abnormal behavior by cross-checking the history of their previous interactions and exploits the fact that nodes pick neighbors at random to prevent colluding nodes from mutually covering up their bad actions.We present a methodology for setting the parameters of LiFTinG to their optimal value, based on a theoretical analysis and we quantify theoretically the performance of LiFTinG. We derive, based on simulations, the optimal strategy of freeriders by taking into account, through a utility function, the benefit of freeriding and the probability of being detected. In addition to these simulations, we report on the deployment of LiFTinG on PlanetLab. In a 300-node system, where a stream of 674 kbps is broadcasted, LiFTinG incurs a maximum Contents lists available at ScienceDirectComputer Networks j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / c o m n e t overhead of only 8% and provides good detection results: For instance, with 10% of freeriders decreasing their contribution by up to 30%, LiFTinG detects 86% of the freeriders after only 30 s and wrongfully expels only a few honest nodes (most of them actually being buggy).

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Tribler

Cited by 13 publications

References 1 publication

Decentralized credit mining in P2P systems

Decentralized credit mining in P2P systems

Byzantine-resilient dual gossip membership management in clouds

Tracking freeriders in gossip-based content dissemination systems

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