1,818 research outputs found

    Counteracting free riding in pure peer-to-peer networks

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    Ankara : The Department of Computer Engineering and The Institute of Engineering and Science of Bilkent University, 2008.Thesis (Ph.D.) -- Bilkent University, 2008.Includes bibliographical references leaves 119-127.The peer-to-peer (P2P) network paradigm has attracted a significant amount of interest as a popular and successful alternative to traditional client-server model for resource sharing and content distribution. However, researchers have observed the existence of high degrees of free riding in P2P networks which poses a serious threat to effectiveness and efficient operation of these networks, and hence to their future. Therefore, eliminating or reducing the impact of free riding on P2P networks has become an important issue to investigate and a considerable amount of research has been conducted on it. In this thesis, we propose two novel solutions to reduce the adverse effects of free riding on P2P networks and to motivate peers to contribute to P2P networks. These solutions are also intended to lead to performance gains for contributing peers and to penalize free riders. As the first solution, we propose a distributed and localized scheme, called Detect and Punish Method (DPM), which depends on detection and punishment of free riders. Our second solution to the free riding problem is a connection-time protocol, called P2P Connection Management Protocol (PCMP), which is based on controlling and managing link establishments among peers according to their contributions. To evaluate the proposed solutions and compare them with other alternatives, we developed a new P2P network simulator and conducted extensive simulation experiments. Our simulation results show that employing our solutions in a P2P network considerably reduces the adverse effects of free riding and improves the overall performance of the network. Furthermore, we observed that P2P networks utilizing the proposed solutions become more robust and scalable.Karakaya, K MuratPh.D

    On the Applicability of Resources Optimization Model for Mitigating Free Riding in P2P System

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    The survival of peer-to-peer systems depends on the contribution of resources by all the participating peers. Selfish behavior of some peers that do not contribute resources inhibits the expected level of service delivery. Free riding has been found to seriously affect the performance and negates the sharing principle of peer-to-peer networks. In this paper, first, we investigate through simulations the effectiveness of a proposed linear model for mitigating free riding in a P2P system. Second, we extended the initial linear model by incorporating additional constraints on download and upload of each peer. This helps in reducing the effects of free riding behavior on the system. Lastly, we evaluate the impacts of some parameters on the models.Keywords: Peer-to-Peer, Resources, Free rider, Optimization, Constraints, Algorith

    The state of peer-to-peer network simulators

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    Networking research often relies on simulation in order to test and evaluate new ideas. An important requirement of this process is that results must be reproducible so that other researchers can replicate, validate and extend existing work. We look at the landscape of simulators for research in peer-to-peer (P2P) networks by conducting a survey of a combined total of over 280 papers from before and after 2007 (the year of the last survey in this area), and comment on the large quantity of research using bespoke, closed-source simulators. We propose a set of criteria that P2P simulators should meet, and poll the P2P research community for their agreement. We aim to drive the community towards performing their experiments on simulators that allow for others to validate their results

    `Q-Feed' - An Effective Solution for the Free-riding Problem in Unstructured P2P Networks

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    This paper presents a solution for reducing the ill effects of free-riders in decentralised unstructured P2P networks. An autonomous replication scheme is proposed to improve the availability and enhance system performance. Q-learning is widely employed in different situations to improve the accuracy in decision making by each peer. Based on the performance of neighbours of a peer, every neighbour is awarded different levels of ranks. At the same time a low-performing node is allowed to improve its rank in different ways. Simulation results show that Q-learning based free riding control mechanism effectively limits the services received by free-riders and also encourages the low-performing neighbours to improve their position. The popular files are autonomously replicated to nodes possessing required parameters. Due to this improvement of quantity of popular files, free riders are given opportunity to lift their position for active participation in the network for sharing files. Q-feed effectively manages queries from free riders and reduces network traffic significantlyComment: 14 pages, 10 figure

    Peer-to-Peer Networks and Computation: Current Trends and Future Perspectives

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    This research papers examines the state-of-the-art in the area of P2P networks/computation. It attempts to identify the challenges that confront the community of P2P researchers and developers, which need to be addressed before the potential of P2P-based systems, can be effectively realized beyond content distribution and file-sharing applications to build real-world, intelligent and commercial software systems. Future perspectives and some thoughts on the evolution of P2P-based systems are also provided

    Accountable Care Organizations are too small and loosely affiliated for financial bonuses to be effective at improving performance.

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    One part of the Affordable Care Act – or Obamacare – has an attempt to tackle the fragmentation of US health care delivery through the introduction of Accountable Care Organizations (ACOs). These organizations contract to provide care to large groups of Medicare recipients and there are group incentives for care to be provided more cost effectively. In new research Brigham Frandsen and James B. Rebitzer find that ACO’s relatively large sizes and loose setups mean that more powerful incentives are needed to reach cost-reduction targets. They write that for an ACO to achieve a cost reduction of 6 percent, bonuses would need to exceed 12 percent of costs – twice the intended savings

    Study of the Topology Mismatch Problem in Peer-to-Peer Networks

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    The advantages of peer-to-peer (P2P) technology are innumerable when compared to other systems like Distributed Messaging System, Client-Server model, Cloud based systems. The vital advantages are not limited to high scalability and low cost. On the other hand the p2p system suffers from a bottle-neck problem caused by topology mismatch. Topology mismatch occurs in an unstructured peer-to-peer (P2P) network when the peers participating in the communication choose their neighbors in random fashion, such that the resultant P2P network mismatches its underlying physical network, resulting in a lengthy communication between the peers and redundant network traffics generated in the underlying network[1] However, most P2P system performance suffers from the mismatch between the overlays topology and the underlying physical network topology, causing a large volume of redundant traffic in the Internet slowing the performance. This paper surveys the P2P topology mismatch problems and the solutions adapted for different applications

    Free riding in peer-to-peer networks

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    Free riding in peer-to-peer (P2P) networks poses a serious threat to their proper operation. Here, the authors present a variety of approaches developed to overcome this problem. They introduce several unique aspects of P2P networks and discuss free riding's effects on P2P services. They categorize proposed solutions and describe each category's important features and implementation issues together with some sample solutions. They also discuss open issues, including common attacks and security considerations. © 2009 IEEE
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