FairNet – How to Counter Free Riding in Peer-to-Peer Data Structures

Buchmann, Erik; Böhm, Klemens

doi:10.1007/978-3-540-30468-5_22

Cited by 17 publications

(12 citation statements)

References 16 publications

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“…A broad class of differential service incentives have been recently studied in order to convey the free-riding problem in P2P resource sharing [8], [2], [5]. A first attempt to formally prove the efficiency of such differentiation mechanisms, using a game theoretic framework, has been proposed by Buragohain et al [6].…”

Section: Related Workmentioning

confidence: 99%

Incentives for P2P Fair Resource Sharing

Anceaume

Gradinariu

Ravoaja

Fifth IEEE International Conference on Peer-to-Peer Computing (P2P'05)

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Abstract-We consider the problem of Fair ResourceSharing to optimize the performance of resource sharing in peer to peer systems. Resource sharing systems currently face rational peers which may exhibit a variety of strategies including : no participation, also referred as free-riding, and greedy behavior. The first aspect has been extensively studied in the late years, while the second one has not received much attention. The broad class of proposed solutions focus on designing incentives to reward cooperative peers. The side effect of these incentives is twofold : the system load is not balanced and the resource potential of the system is not fully exploited. The P2P fair resource sharing aims at both balancing the load and maximizing the use of system resources.The contribution of our work is twofold. First, we specify the P2P Fair Resource Sharing problem and propose a mechanism to solve it in large scale dynamic networks with rational users. Our mechanism is composed of a novel incentive (i.e. fair cooperation) and an algorithmic part encapsulated in a middleware layer. Second, we propose an architecture for our mechanism middleware layer including four distributed services that bring together several research area : aggregation, semantic group membership and tracking. Finally, we implement our mechanism using a peer-to-peer unstructured model and evaluate it through simulations.

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Section: Related Workmentioning

confidence: 99%

Incentives for P2P Fair Resource Sharing

Anceaume

Gradinariu

Ravoaja

Fifth IEEE International Conference on Peer-to-Peer Computing (P2P'05)

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“…Second, several nodes have to cooperate to answer a query. Under realistic assumptions, if only 5% of the nodes are uncooperative, 40% of the queries in such a system remain unanswered [4].…”

Section: Figure 1: Content-addressable Networkmentioning

confidence: 99%

Competition vs. Fairness  Analyzing Structured Networks by Means of User Experiments

Schosser

Böhm

Vogt

2008

2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology

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We investigate how to ensure efficiency (in the economic sense of the word) in structured networks, with a focus on heterogeneity. A network is structured if the network designer has predefined some relationships between individuals (aka. nodes). Structured networks have turned out to be surprisingly efficient -at least as long as nodes face the same costs and benefits, i.e., are homogeneous [25]. However, the homogeneity assumption is unnatural and restrictive. Economic experiments in general (not with a focus on structured networks) suggest that heterogeneity is in the way of efficiency, i.e., reduces the sum of all payoffs. This is because individuals favor outcomes where everybody earns the same. This paper describes behavioral experiments that investigate this issue, i.e., the influence of heterogeneity on efficiency in structured networks. We show that most nodes in structured networks cooperate even if they earn less than others. Our explanation is that -with our design -competition enhances cooperation. This effect is rarely observed with other networks as well as in other, less specific settings where competition is in the way of cooperation. This result is an important step towards establishing networks that yield more tangible payoffs for its nodes.

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“…The peers monitor the way their neighbors forward/answer queries in order to detect freeriders and query droppers. In [8], the authors propose a protocol to detect and exclude freeriders in the CAN peer-to-peer data structure by monitoring how peers forward and answer routing messages. Yet, contrarily to LiFTinG -which is based on random peer selection -in both protocols the peers's neighborhoods are static, forming a fixed mesh overlay.…”

Section: Related Workmentioning

confidence: 99%

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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FairNet – How to Counter Free Riding in Peer-to-Peer Data Structures

Cited by 17 publications

References 16 publications

Incentives for P2P Fair Resource Sharing

Incentives for P2P Fair Resource Sharing

Competition vs. Fairness  Analyzing Structured Networks by Means of User Experiments

Tracking freeriders in gossip-based content dissemination systems

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FairNet – How to Counter Free Riding in Peer-to-Peer Data Structures

Cited by 17 publications

References 16 publications

Incentives for P2P Fair Resource Sharing

Incentives for P2P Fair Resource Sharing

Competition vs. Fairness &#150; Analyzing Structured Networks by Means of User Experiments

Tracking freeriders in gossip-based content dissemination systems

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Product

Resources

About

Competition vs. Fairness Analyzing Structured Networks by Means of User Experiments