Predicting the level of cooperation in a Peer-to-Peer live streaming application

Gonçalves, Glauber Dias; Cunha, Ítalo; Vieira, Alex Borges; Almeida, Jussara M.

doi:10.1007/s00530-014-0434-5

Cited by 13 publications

(7 citation statements)

References 43 publications

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“…In the following, we discuss the application domains and incentive enforcement types, i.e., Individual Reciprocity, Clustered Cooperation, Local Denylisting, and Global Denylisting, of reputation mechanisms. Reviewed works predominantly applied reputation-based incentives for file sharing [5,11,42,43,58,62,63,75,77,84,92,109,117,127] and media streaming [6,11,30,36,42,52,65,72,79,87,93,101,117,118,132,136]. Only two articles covered peerto-peer computing [76,97,102], and service discovery [24,98], respectively.…”

Section: Reputationmentioning

confidence: 99%

“…Peers are assigned to super peers based on their reputation value, physical proximity, thematic similarity, common interest, or at random. Other approaches put peers on a global, i.e., network-wide, denylist [6,36,52,55,127], or allow peers to dismiss uncooperative peers locally [19,24,30,57,92,98,111,121,[140][141][142]. An approach that allows global peer penalties requires a single source of truth available to all participants.…”

Section: Reputationmentioning

confidence: 99%

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Incentive Mechanisms in Peer-to-Peer Networks — A Systematic Literature Review

et al. 2023

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Centralized networks inevitably exhibit single points of failure that malicious actors regularly target. Decentralized networks are more resilient if numerous participants contribute to the network’s functionality. Most decentralized networks employ incentive mechanisms to coordinate the participation and cooperation of peers and thereby ensure the functionality and security of the network. This article systematically reviews incentive mechanisms for decentralized networks and networked systems by covering 165 prior literature reviews and 178 primary research papers published between 1993 and October 2022. Of the considered sources, we analyze eleven literature reviews and 105 primary research papers in detail by categorizing and comparing the distinctive properties of the presented incentive mechanisms. The reviewed incentive mechanisms establish fairness and reward participation and cooperative behavior. We review work that substitutes central authority through independent and subjective mechanisms run in isolation at each participating peer and work that applies multiparty computation. We use monetary, reputation, and service rewards as categories to differentiate the implementations and evaluate each incentive mechanism’s data management, attack resistance, and contribution model. Further, we highlight research gaps and deficiencies in reproducibility and comparability. Finally, we summarize our assessments and provide recommendations to apply incentive mechanisms to decentralized networks that share computational resources.

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Section: Reputationmentioning

confidence: 99%

Section: Reputationmentioning

confidence: 99%

Incentive Mechanisms in Peer-to-Peer Networks — A Systematic Literature Review

et al. 2023

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“…Most works apply machine learning techniques to explore traffic features and improve the classification performance. For example, Goncalves et al [15] used a regressionbased model to divide the traffic into Peer-to-Peer (P2P) traffic and non-P2P traffic. Tang et al [16] employed the fractal features to identify video traffic and good results were obtained by different machine learning based classifiers.…”

Section: A Machine Learning Based Traffic Classificationmentioning

confidence: 99%

Multimedia Traffic Classification for Imbalanced Environment

Yang

Jin

et al. 2022

IEEE Trans. Netw. Sci. Eng.

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With ever-increasing volume and variety of multimedia traffic on the Internet, machine learning-empowered techniques nowadays tend to become indispensable for future intelligent network management. To realize automatic traffic management with Quality of Service (QoS) guarantees, there is a pressing need for accurate traffic classification. However, the inherent characteristics of networks cause imbalanced class distribution in traffic classification, which could degrade the performance of classification, especially on the minority classes. To address the issue of class imbalance in both stationary and nonstationary environments, this paper proposes a novel scheme called CHS (chain hierarchical structure) which is able to characterize class distribution from a new perspective. By building an error model, we can compute the error propagation generated by CHS and analyze the factors that affect it. More importantly, two key methods involving classifier ranking and combination with the hierarchical structure are devised to mitigate the error propagation produced by the classifier. The effectiveness of the developed framework is validated through experiments over two real-world traffic datasets in both stationary and nonstationary environments. The experimental results demonstrate that our proposed methods outperform the state-of-the-art approaches in terms of classification accuracy and running time. The proposed methods are particularly effective in the nonstationary imbalanced environment.

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“…A successful trust management system that uses this approach is [20]. The [16], [21] are other examples of trust management systems. EigenTrust is the name of an approach proposed In [16].…”

Section: Related Workmentioning

confidence: 99%

“…EigenTrust is able to decrease the number of inauthentic files on the network, even under a variety of conditions where malicious peers cooperate in an attempt to subvert the system. In [21], the authors have introduced an approach to predict a peer's cooperation level, focusing on the cooperation induced by the P2P protocol rather than the cooperation that results from user behavior or bandwidth limitation. This method mainly focuses on live streaming applications.…”

Section: Related Workmentioning

confidence: 99%

An Incentive Security Model to Provide Fairness for Peer-to-Peer Networks

Berenjian¹,

Hajizadeh²,

Atani³

2019

Preprint

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Peer-to-Peer networks are designed to rely on resources of their own users. Therefore, resource management plays an important role in P2P protocols. Therefore, resource management plays an important role in P2P protocols. Early P2P networks did not use proper mechanisms to manage fairness. However, after seeing difficulties and rise of freeloaders in networks like Gnutella, the importance of providing fairness for users have become apparent. In this paper, we propose an incentive based security model which leads to a network infrastructure that lightens the work of Seeders and makes Leechers to contribute more. This method is able to prevent betrayals in Leecher-to-Leecher transactions and more importantly, helps Seeders to be treated more fairly. This is what other incentive methods such as Bittorrent are incapable of doing. Additionally, by getting help from cryptography and combining it with our method, it is also possible to achieve secure channels, immune to spying, next to a fair network. The simulation results clearly show that how our proposed approach can overcome free-riding issue. In addition, our findings revealed that our approach is able to provide an appropriate level of fairness for the users and can decrease the download time.

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Predicting the level of cooperation in a Peer-to-Peer live streaming application

Cited by 13 publications

References 43 publications

Incentive Mechanisms in Peer-to-Peer Networks — A Systematic Literature Review

Incentive Mechanisms in Peer-to-Peer Networks — A Systematic Literature Review

Multimedia Traffic Classification for Imbalanced Environment

An Incentive Security Model to Provide Fairness for Peer-to-Peer Networks

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