Efficient message flooding on DHT network

Ching Wei Huang*, Wuu Yang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

For the high scalability, DHT network becomes popular in P2P development in these few years. In comparing to flood ing-based searching in unstructured P2P network, DHT network provides an efficient lookup. However, flooding still plays an important role in P2P systems. Some fundamental functions such as information collection, dissemination, or keyword searching can benefit from an efficient flooding mechanism. In this paper, we present a DHT network in which one flooding request generates O(N) messages where N is the system size. Moreover, our method considers message locality. Message forwarding across different autonomous systems are reduced significantly. Base on our flooding mechanism, information broadcasting and aggregation are fulfilled without much effort.

Original languageEnglish
Title of host publicationHigh Performance Computing and Communcations - First International Conference, HPCC 2005, Proceedings
Pages440-454
Number of pages15
DOIs
StatePublished - 1 Dec 2005
Event1st International Conference on High Performance Computing and Communcations, HPCC 2005 - Sorrento, Italy
Duration: 21 Sep 200523 Sep 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3726 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Conference on High Performance Computing and Communcations, HPCC 2005
CountryItaly
CitySorrento
Period21/09/0523/09/05

Keywords

  • Aggregation
  • Broadcast
  • DHT
  • Flooding
  • Peer-to-Peer System

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  • Cite this

    Huang, C. W., & Yang, W. (2005). Efficient message flooding on DHT network. In High Performance Computing and Communcations - First International Conference, HPCC 2005, Proceedings (pp. 440-454). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3726 LNCS). https://doi.org/10.1007/11557654_53