Topology-Sensitive Epidemic Algorithm for Information Spreading in Large-Scale Systems

  • J. Acosta-Elías
  • J. M. Luna-Rivera
  • M. Recio-Lara
  • O. Gutiérrez-Navarro
  • B. Pineda-Reyes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4330)


Epidemic algorithms are an emerging technique that has recently gained popularity as a potentially effective solution for disseminating information in large-scale network systems. For some application scenarios, efficient and reliable data dissemination to all or a group of nodes in the network is necessary to provide with the communication services within the system. These studies may have a large impact in communication networks where epidemic-like protocols become a practice for message delivery, collaborative peer-to-peer applications, distributed database systems, routing in Mobile Ad Hoc networks, etc. In this paper we present, through various simulations, that an epidemic spreading process can be highly influenced by the network topology. We also provide a comparative performance analysis of some global parameters performance such as network diameter and degree of connectivity. Based on this analysis, we propose a new epidemic strategy that takes into account the topological structure in the network. The results show that the proposed epidemic algorithm outperform a classical timestamped anti-entropy epidemic algorithm in terms of the number of sessions required to reach a consistent state in the network system.


Network Topology Neighbor Node Data Dissemination Server Node Ring Topology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Pastor-Satorras, R., Vespignant, A.: Epidemic Spreading in Scale Free Networks. Physica Review Letters 86 (2001)Google Scholar
  2. 2.
    Pool, I., Kochen, M.: Contacts and influence. Social Networks 1(5), 51 (1978)Google Scholar
  3. 3.
    Wu, F., Huberman, B.A., Adamic, L.A., Tyler, J.R.: Information Flow in Social Groups. In: Annual CNLS conference on Networks, Santa Fe, NM, May 12 (2003),
  4. 4.
    Euster, P.T., Guerraoui, R., Kermarrec, A.-M., Mussoulie, K.L.: Epidemic Information Dissemination in Distributed Systems. Computer 37(5), 60–67 (2004)CrossRefGoogle Scholar
  5. 5.
    Li, L., Halpern, J., Hass, Z.J.: Gossip-based ad hoc routing. In: Proceedings of Infocom, pp. 1707–1716 (2002)Google Scholar
  6. 6.
    Cuenca-Acuna, F.M., Peery, C., Martin, R.P., Nguyen, T.D.: Using Gossiping to Build Content Addressable Peer-to-Peer Information Sharing Communities. In: Procidings of the IEEE International Symposium on High Performance Distributed Computing (HPCD 12), Seattle, WA (June 2003)Google Scholar
  7. 7.
    Ganesh, A., Kermarrec, A., Massoulie, L.: Peer-to-Peer Membership Management for Gossip-based Protocols. IEEE Trans. Comp. 52(2), 139–149 (2003)CrossRefGoogle Scholar
  8. 8.
    Foster, I., Kesselman, C., Tuecke, S.: The Anatomy of the Grid: Enabling Scalable Virtual Organizations International. J. Supercomputer Applications 15(3) (2001)Google Scholar
  9. 9.
    Golding, R.A.: Weak-Consistency Group Communication and Membership. PhD thesis, University of California, Santa Cruz, Computer and Information Sciences Technical Report UCSC-CRL-92-52 (December 1992)Google Scholar
  10. 10.
    BRITE. The Boston Representative Internet Topology Generator,
  11. 11.
    Adya, A.: Weak Consistency: A Generalized Theory and Optimistic Implementations for Distributed Transactions. PhD thesis M. I. T., Department of Electrical Engineering and Computer Science (March 1999)Google Scholar
  12. 12.
    Guy, R., Heidemann, J., Mak, W., Page Jr., T., Popek, G., Rothmeier, D.: Implementation of the Ficus Replicated File System. In: Proceedings Summer USENIX Conf. (June 1990)Google Scholar
  13. 13.
    Holliday, J., Agrawal, D., Abbadi, A.E.: Partial Database Replication using Epidemic Communication. In: Proceedings of the 22nd International Conference on Distributed Computing Systems (ICDCS 2002), Vienna (2002)Google Scholar
  14. 14.
    Marques, J.M., Navarro, L.: Autonomous and Self-sufficient Groups: Ad Hoc Collaborative Environments. In: 11th International Workshop on Groupware (CRIWG-2005) (September 2005)Google Scholar
  15. 15.
    The Network Simulator 2,
  16. 16.
    Lamport, L.: Time, clocks, and the ordering of events in a distributed system. Communications of the ACM 21(7), 558 (1978)MATHCrossRefGoogle Scholar
  17. 17.
    Barabási, B.A.-L., Albert, R.: Emergence of Scaling in Random Networks. Science, 509–512 (October 1999)Google Scholar
  18. 18.
    Faloutsos, A., Siganos, G., Faloutsos, M., Faloutsos, P., Faloutsos, C.: Power laws and the AS-level internet topology. IEEE/ACM, Transactions on Networking 11(4), 514–524 (2003)CrossRefGoogle Scholar
  19. 19.
    Acosta-Elías, J., Pineda, U., Luna-Rivera, J.M., Stevens-Navarro, E., Campos-Canton, I., Navarro-Moldes, L.: The Effects of Network Topology on Epidemic Algorithms. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds.) ICCSA 2004. LNCS, vol. 3046, pp. 177–184. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • J. Acosta-Elías
    • 1
  • J. M. Luna-Rivera
    • 1
  • M. Recio-Lara
    • 1
  • O. Gutiérrez-Navarro
    • 1
  • B. Pineda-Reyes
    • 1
  1. 1.Facultad de Ciencias de la Universidad Autónoma de San Luis PotosíSan Luis Potosí, S.L.P.México

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