Optimal Resource Discovery Paths of Gnutella2

Vapa, M.; Auvinen, Annemari; Ivanchenko, Y.; Kotilainen, Niko; Vuori, J.

doi:10.1109/aina.2008.89

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Gossip-based Search: Analysis and Proof1

Peer-to-peer Realm1

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2015

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Cited by 3 publications

(2 citation statements)

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“…In this section, Gossip-based search algorithm routing process is introduced in the view of information retrieval. We describe and analyze it based on Markov inequality theory [15] and Jensen inequality theory [16] , we analyze and prove the gossip algorithm in terms of matrix theory and time complexity.…”

Section: Gossip-based Search: Analysis and Proofmentioning

confidence: 99%

Gossip‐Based Resource Location Strategy in Interest Community for P2P Networks

He¹,

Zhang²,

Meng³

2015

Chin. j. electron.

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Section: Gossip-based Search: Analysis and Proofmentioning

confidence: 99%

Gossip‐Based Resource Location Strategy in Interest Community for P2P Networks

He¹,

Zhang²,

Meng³

2015

Chin. j. electron.

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“…The first results of this kind of an algorithm development was reported in [23]. Also, the simulator contains implementations of various P2P resource discovery algorithms such as Breadth-First Search [13], Random Walker [12], Highest Degree Search [1,22] and optimal path K-Steiner Tree approximation [22]. These algorithms can be used as performance measures for neural network based algorithms or for studying their performance in different P2P network scenarios.…”

Section: Peer-to-peer Realmmentioning

confidence: 99%

P2PRealm - Peer-to-Peer Network Simulator

Kotilainen

Vapa

Keltanen

et al.

2006 11th Intenational Workshop on Computer-Aided Modeling, Analysis and Design of Communication Links and Networks

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Abstract-Peer-to-Peer Realm (P2PRealm) is an efficient peer-topeer 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 implementation and the configuration used for distribution. Finally, we present the use of P2PRealm with the P2PStudio network visualization tool.

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