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Highly intensive data dissemination in complex networks



This paper presents a study on data dissemination in unstructured Peer-to-Peer (P2P) network overlays. The absence of a structure in unstructured overlays eases the network management, at the cost of non-optimal mechanisms to spread messages in the network. Thus, dissemination schemes must be employed that allow covering a large portion of the network with a high probability (e.g.~gossip based approaches). We identify principal metrics, provide a theoretical model and perform the assessment evaluation using a high performance simulator that is based on a parallel and distributed architecture. A main point of this study is that our simulation model considers implementation technical details, such as the use of caching and Time To Live (TTL) in message dissemination, that are usually neglected in simulations, due to the additional overhead they cause. Outcomes confirm that these technical details have an important influence on the performance of dissemination schemes and that the studied schemes are quite effective to spread information in P2P overlay networks, whatever their topology. Moreover, the practical usage of such dissemination mechanisms requires a fine tuning of many parameters, the choice between different network topologies and the assessment of behaviors such as free riding. All this can be done only using efficient simulation tools to support both the network design phase and, in some cases, at runtime.


  • Data dissemination, Simulation, Complex Networks, Performance Evaluation


  • Published in the Journal of Parallel and Distributed Computing, Volume 99, January 2017, Pages 28-50, ISSN 0743-7315,


  • A pre-peer reviewed version of the article can be found on arXiv:1507.08417.
  • The publisher version is available at [link].


  • The LUNES source code is part of the ARTÌS software distribution
  • Raw data obtained by the experiments, graph corpuses, processed data and scripts used to produce the figures: download
gossip-paper.txt · Last modified: 2020/01/17 10:33 by gda