1,936 research outputs found
Mathematical modeling of incentive policies in P2P systems.
Zhao, Qiao.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references (leaves 35-36).Abstracts also in Chinese.Abstract --- p.iAcknowledgement --- p.vChapter 1 --- Introduction --- p.1Chapter 2 --- Model Description --- p.3Chapter 2.1 --- An Incentive Model for P2P Networks --- p.3Chapter 2.2 --- Learning Models for P2P Networks --- p.5Chapter 2.2.1 --- Current-best Learning Model (CBLM) --- p.5Chapter 2.2.2 --- Opportunistic Learning Model (OLM) --- p.6Chapter 2.3 --- Incentive Policies for P2P Networks --- p.7Chapter 2.3.1 --- Mirror Incentive Policy Vmirror --- p.8Chapter 2.3.2 --- Proportional Incentive Policy Vprop --- p.9Chapter 2.3.3 --- Linear Incentive Policy Class CLIP --- p.9Chapter 2.4 --- Performance and Robustness of Incentive Policies --- p.10Chapter 2.4.1 --- Robustness Analysis of Mirror Incentive Policy using the current-best learning method --- p.10Chapter 2.4.2 --- Robustness Analysis of Mirror Incentive Policy using the opportunistic learning method --- p.12Chapter 2.4.3 --- Robustness Analysis of Proportional Incentive Policy Using the current-best learning method --- p.12Chapter 2.4.4 --- Robustness Analysis of Proportional Incentive Policy Using the opportunistic learning method --- p.13Chapter 2.4.5 --- Robustness Analysis for Incentive Protocol in the Linear Incentive Class --- p.14Chapter 2.5 --- Connection with Evolutionary Game Theory --- p.17Chapter 3 --- Performance Evaluation --- p.21Chapter 3.1 --- Performance and Robustness of the Mirror Incentive Policy (Pmirror): --- p.21Chapter 3.2 --- Performance and Robustness of the Proportional Incentive Policy {Pprop): --- p.23Chapter 3.3 --- Performance and Robustness of incentive policy in the Linear Incentive Class (CLIP): --- p.24Chapter 3.4 --- The Effect of Non-adaptive Peers: --- p.25Chapter 4 --- Adversary Effect of Altruism --- p.29Chapter 4.1 --- The Effect of Protocol Cost --- p.29Chapter 4.2 --- The Tradeoff between Altruism and System Robustness --- p.30Chapter 5 --- Related Work --- p.33Chapter 6 --- Conclusion --- p.34Bibliography --- p.3
On the Topology Maintenance of Dynamic P2P Overlays through Self-Healing Local Interactions
This paper deals with the use of self-organizing protocols to improve the
reliability of dynamic Peer-to-Peer (P2P) overlay networks. We present two
approaches, that employ local knowledge of the 2nd neighborhood of nodes. The
first scheme is a simple protocol requiring interactions among nodes and their
direct neighbors. The second scheme extends this approach by resorting to the
Edge Clustering Coefficient (ECC), a local measure that allows to identify
those edges that connect different clusters in an overlay. A simulation
assessment is presented, which evaluates these protocols over uniform networks,
clustered networks and scale-free networks. Different failure modes are
considered. Results demonstrate the viability of the proposal.Comment: A revised version of the paper appears in Proc. of the IFIP
Networking 2014 Conference, IEEE, Trondheim, (Norway), June 201
The state of peer-to-peer network simulators
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
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Self-organizing peer-to-peer social networks
This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2008 The Authors.Peer-to-peer (P2P) systems provide a new solution to distributed information and resource sharing because of its outstanding properties in decentralization, dynamics, flexibility, autonomy, and cooperation, summarized as DDFAC in this paper. After a detailed analysis of the current P2P literature, this paper suggests to better exploit peer social relationships and peer autonomy to achieve efficient P2P structure design. Accordingly, this paper proposes Self-organizing peer-to-peer social networks (SoPPSoNs) to self-organize distributed peers in a decentralized way, in which neuron-like agents following extended Hebbian rules found in the brain activity represent peers to discover useful peer connections. The self-organized networks capture social associations of peers in resource sharing, and hence are called P2P social networks. SoPPSoNs have improved search speed and success rate as peer social networks are correctly formed. This has been verified through tests on real data collected from the Gnutella system. Analysis on the Gnutella data has verified that social associations of peers in reality are directed, asymmetric and weighted, validating the design of SoPPSoN. The tests presented in this paper have also evaluated the scalability of SoPPSoN, its performance under varied initial network connectivity and the effects of different learning rules.National Natural Science of Foundation of Chin
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