183,912 research outputs found
A stochastic epidemiological model and a deterministic limit for BitTorrent-like peer-to-peer file-sharing networks
In this paper, we propose a stochastic model for a file-sharing peer-to-peer
network which resembles the popular BitTorrent system: large files are split
into chunks and a peer can download or swap from another peer only one chunk at
a time. We prove that the fluid limits of a scaled Markov model of this system
are of the coagulation form, special cases of which are well-known
epidemiological (SIR) models. In addition, Lyapunov stability and settling-time
results are explored. We derive conditions under which the BitTorrent
incentives under consideration result in shorter mean file-acquisition times
for peers compared to client-server (single chunk) systems. Finally, a
diffusion approximation is given and some open questions are discussed.Comment: 25 pages, 6 figure
A Mechanism that Provides Incentives for Truthful Feedback in Peer-to-Peer Systems
We propose a mechanism for providing the incentives for reporting truthful feedback in a peer-to-peer system for exchanging services (or content). This mechanism is to complement reputation mechanisms that employ ratings' feedback on the various transactions in order to provide incentives to peers for offering better services to others. Under our approach, each of the transacting peers (rather than just the client) submits a rating on the performance of their mutual transaction. If these are in disagreement, then both transacting peers are punished, since such an occasion is a sign that one of them is lying. The severity of each peer's punishment is determined by his corresponding non- credibility metric; this is maintained by the mechanism and evolves according to the peer's record. When under punishment, a peer does not transact with others. We model the punishment effect of the mechanism in a peer-to-peer system as a Markov chain that is experimentally proved to be very accurate. According to this model, the credibility mechanism leads the peer-to-peer system to a desirable steady state isolating liars. Then, we define a procedure for the optimization of the punishment parameters of the mechanism for peer-to-peer systems of various characteristics. We experimentally prove that this optimization procedure is effective and necessary for the successful employment of the mechanism in real peer-to-peer systems. Then, the optimized credibility mechanism is combined with reputation-based policies to provide a complete solution for high performance and truthful rating in peer-to-peer systems. The combined mechanism was experimentally proved to deal very effectively with large fractions of collaborated liar peers that follow static or dynamic rational lying strategies in peer-to-peer systems with dynamically renewed population, while the efficiency loss induced to sincere peers by the presence of liars is diminished. Finally, we describe the potential implementation of the mechanism in real peer-to-peer systems
An Effective Peer to Peer Video Sharing Scheme with Social Reciprocity
Online video sharing and social networking are self-fertilizing speedily in today’s Internet. Online social network users are flooding more video contents among each other. A fascinating development as it is, the operational challenge in previous video streaming systems persists, i.e., the large server load required for topping of the systems. Exploring the unique advantages of a social networking based video streaming system; it advocate utilizing social reciprocities among peers with social relationships for efficient involvement incentivization and development, so as to enable high quality video streaming with low server cost. Then why only video: because more people prefer watching videos. Videos induce people to stay longer on websites. People remember videos. It achievement social reciprocity with two give-and-take ratios at each peer: (1) peer contribution ratio (PCR), which calculates the reciprocity level between a couple of social friends, and (2) system contribution ratio (SCR), which records the give-and-take level of the user to & from the entire system. It expect efficient Peer to Peer mechanisms for video streaming using the two ratios, where each user optimally chooses which other users to seek relay help from and help in relaying video streams, respectively, based on combined evaluations of their social relationship and historical reciprocity levels. This design helps to gain effective incentives for resource contribution, load balancing among relay peers, and efficient social-aware resource scheduling, security to the videos and high prefetching accuracy.
DOI: 10.17762/ijritcc2321-8169.15071
Corrective or critical? Commenting on bad questions in Q&A
What kind of comments do Q&A community members prefer on bad questions? We studied this question on Stack Overflow, a large-scale community-run question-answer site with strong reputation and privilege systems and clear guidelines on commenting. Peer-production systems often employ feedback dialogue to engage with producers of low quality content. However, dialogue is only beneficial if it is constructive, as previous work has shown the adverse effects of negative feedback on quality and production. Previous studies indicate that feedback is likely critical, but the extent, orientation, and actors within this assumption are unknown. In this paper, we contribute a basic taxonomy of commenting and perform analysis on user types and community preferences. Results indicate that the most popular and frequent comments include criticism, and that different user types leave similar feedback. A better understanding of community feedback norms can inform the design of effective rules and incentives for peer-production systems
Peer-assisted online games with social reciprocity
Online games and social networks are cross-pollinating rapidly in today's Internet: Online social network sites are deploying more and more games in their systems, while online game providers are leveraging social networks to power their games. An intriguing development as it is, the operational challenge in the previous game persists, i.e., the large server operational cost remains a non-negligible obstacle for deploying high-quality multi-player games. Peer-to-peer based game network design could be a rescue, only if the game players' mutual resource contribution has been fully incentivized and efficiently scheduled. Exploring the unique advantage of social network based games (social games), we advocate to utilize social reciprocities among peers with social relationships for efficient contribution incentivization and scheduling, so as to power a high-quality online game with low server cost. In this paper, social reciprocity is exploited with two give-and-take ratios at each peer: (1) peer contribution ratio (PCR), which evaluates the reciprocity level between a pair of social friends, and (2) system contribution ratio (SCR), which records the give-and-take level of the player to and from the entire network. We design efficient peer-to-peer mechanisms for game state distribution using the two ratios, where each player optimally decides which other players to seek relay help from and help in relaying game states, respectively, based on combined evaluations of their social relationship and historical reciprocity levels. Our design achieves effective incentives for resource contribution, load balancing among relay peers, as well as efficient social-aware resource scheduling. We also discuss practical implementation concerns and implement our design in a prototype online social game. Our extensive evaluations based on experiments on PlanetLab verify that high-quality large-scale social games can be achieved with conservative server costs. © 2011 IEEE.published_or_final_versionThe 19th IEEE International Workshop on Quality of Service (IWQoS 2011), San Jose, CA., 6-7 June 2011. In Proceedings of 19th IWQoS, 2011, p. 1-
The need for open source software in machine learning
Open source tools have recently reached a level of maturity which makes them suitable for building
large-scale real-world systems. At the same time, the field of machine learning has developed a
large body of powerful learning algorithms for diverse applications. However, the true potential of
these methods is not used, since existing implementations are not openly shared, resulting in software
with low usability, and weak interoperability. We argue that this situation can be significantly
improved by increasing incentives for researchers to publish their software under an open source
model. Additionally, we outline the problems authors are faced with when trying to publish algorithmic
implementations of machine learning methods. We believe that a resource of peer reviewed
software accompanied by short articles would be highly valuable to both the machine learning and
the general scientific community
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