0000000000053683

AUTHOR

A. Auvinen

showing 4 related works from this author

P2PStudio - Monitoring, Controlling and Visualization Tool for Peer-to-Peer Networks Research

2006

Peer-to-Peer Studio has been developed as a monitoring, controlling and visualization tool for peer-to-peer networks. It uses a centralized architecture to gather events from a peer-to-peer network and can be used to visualize network topology and to send different commands to individual peer-to-peer nodes. The tool has been used with Chedar Peer-to-Peer network to study the behavior of different peer-to-peer resource discovery and topology management algorithms and for visualizing the results of NeuroSearch resource discovery algorithm produced by the Peer-to-Peer Realm network simulator. This paper presents the features, the architecture and the protocols of Peer-to-Peer Studio and the ex…

Computer sciencebusiness.industryP2PStudioDistributed computingPeer-to-peerNetwork topologycomputer.software_genreNetwork simulationVisualizationResource (project management)vertaisverkkoArchitecturebusinesscomputervalvontatyökaluStudioNetwork management stationComputer network
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Optimal Resource Discovery Paths of Gnutella2

2008

This paper shows that the performance of peer-to-peer resource discovery algorithms is upper bounded by a k-Steiner minimum tree and proposes an algorithm locating near-optimal query paths for the peer-to-peer resource discovery problem. Global knowledge of the topology and the resources from the peer-to-peer network are required as an input to the algorithm. The algorithm provides an objective measure for defining how good local search algorithms are. The performance is evaluated in simulated peer-to-peer scenarios and in the measured Gnutella2 P2P network topology with four local search algorithms: breadth-first search, self-avoiding random walker, highest degree search and Dynamic Query …

Theoretical computer sciencebusiness.industryComputer scienceNetwork topologyComputer Science::Digital LibrariesSteiner tree problemTree (graph theory)symbols.namesakeRandom walker algorithmSearch algorithmBounded functionsymbolsResource allocationLocal search (optimization)Gnutella2business22nd International Conference on Advanced Information Networking and Applications (aina 2008)
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P2PRealm - Peer-to-Peer Network Simulator

2006

Peer-to-peer realm (P2PRealm) is an efficient peer-to-peer network simulator for studying algorithms based on neural networks. In contrast to many simulators, which emphasize on detailed network simulation, the speed of simulation in P2PRealm is essential, because neural networks require a time consuming training phase. Efficiency has been obtained by optimizing training loops inside the simulator, using Java native interface (JNI) as well as distributing the simulator to hundreds of workstations using the P2PDisCo platform. In this paper we describe the architecture of P2PRealm and its input/output interfaces. Also, we present the mechanisms used for internally optimizing the implementatio…

JavaComputer architecture simulatorWorkstationArtificial neural networkComputer scienceJava Native InterfaceDistributed computingPeer-to-peercomputer.software_genreNetwork simulationlaw.inventionvertaisverkkosimulaattorilawcomputerPower system simulator for engineeringcomputer.programming_language2006 11th Intenational Workshop on Computer-Aided Modeling, Analysis and Design of Communication Links and Networks
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Topology management in unstructured P2P networks using neural networks

2007

Resource discovery is an essential problem in peer-to-peer networks since there is no centralized index in which to look for information about resources. In a pure P2P network peers act as servers and clients at the same time and in the Gnutella network for example, peers know only their neighbors. In addition to developing different kinds of resource discovery algorithms, one approach is to study the different topologies or structures of the P2P network. In many cases topology management is based on either technical characteristics of the peers or their interests based on the previous resource queries. In this paper, we propose a topology management algorithm which does not predetermine fa…

Intelligent computer networkArtificial neural networkComputer sciencebusiness.industryDistributed computingServerLogical topologyNetwork topologybusinessNetwork management stationNetwork simulationNetwork formationComputer network2007 IEEE Congress on Evolutionary Computation
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