Performance Evaluation of a Self-organized Hierarchical Topology for Update of Replicas in a Large Distributed System

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3758)


In this paper we evaluate our own weak consistency algorithm, which is called the ”Fast Consistency Algorithm”, and whose main aim is optimizing the propagation of changes introducing a preference for nodes and zones of the network which have greatest demand. Weak consistency algorithms allow us to propagate changes in a large, arbitrary changing storage network in a self-organizing way. These algorithms generate very little traffic overhead; they have low latency and are scalable, in addition to being fault tolerant. The algorithm has been simulated over ns-2, and measured its performance for complex spatial distributions of demand, including Internet like self-similar fractal distributions of demand. The impulse response of the algorithm has been characterized. We conclude that considering application parameters such as demand in the event or change propagation mechanism to: 1) prioritize probabilistic interactions with neighbors with higher demand, and 2) including little changes on the logical topology (leader interconnection in hierarchical topology ), gives a surprising improvement in the speed of change propagation perceived by most users. In other words, it satisfies the greatest demand in the shortest amount of time.


High Demand Great Demand Social Welfare Function Ring Topology Star 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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    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
  2. 2.
    Fournier, A., Fussell, D., Carpenter, L.: Computer Rendering of Stochastic Models. Comm. of the ACM 6(6), 371–384 (1982)CrossRefGoogle Scholar
  3. 3.
    Lakhina, A., Byers, J., Crovella, M., Matta, I.: On the Geographic Location of Internet Resources. In: Internet Measurement Workshop 2002 Marseille, France, November 6-8 (2002)Google Scholar
  4. 4.
    Danesh, A., Trajkovic, L., Robin, S.H., Smith, M.H.: Mapping the Internet (2001)Google Scholar
  5. 5.
    Neuman, C.: Scale in Distributed Systems. In Readings in Dist. Comp. Syst. IEEE Computer Society Press, Los Alamitos (1994)Google Scholar
  6. 6.
    Dietterich, J.: DEC data distributor: for data replication and data warehousing. In: Int. Conf. On Management of data, May 1994, p. 468. ACM, New York (1994)Google Scholar
  7. 7.
    Laherrere, J., Sornette, D.: Stretched exponential distributions in Nature and Economy: ’Fat tails’ with characteristic scales. European Physical Jour. B2, 525–539 (1998)Google Scholar
  8. 8.
    Elias, J.A.: Leandro Navarro Moldes: A Demand Based Algorithm for Rapid Updating of Replicas. In: IEEE Workshop on Resource Sharing in Massively Distributed Systems (RESH 2002) (July 2002)Google Scholar
  9. 9.
    Elias, J.A.: Leandro Navarro Moldes: Behaviour of the fast consistency algorithm in the set of replicas with multiple zones with high demand. In: Simp. in Informatics and Telecommunications, SIT 2002 (2002)Google Scholar
  10. 10.
    Elias, J.A.: Leandro Navarro Moldes: Generalization of the fast consistency algorithm to multiple high demand zones. In: Proc. of the Int.Conference on Computational Science 2003 (ICCS 2003), St.Petersburg, Russia, June 2-4 (2003)Google Scholar
  11. 11.
    Birman, K.P.: The process group approach to reliable distributed computing. Communications of ACM 36(12) (December 1993)Google Scholar
  12. 12.
    Petersen, K., Spreitzer, M.J., Terry, D.B., Theimer, M.M., Demers: Flexible Update Propagation for Weakly Consistent Replication. In: Proc. of the 16th ACM Symposium on Operating Systems Principles (SOSP-16), Saint Malo, France, October 5-8, pp. 288–301 (1997)Google Scholar
  13. 13.
    Medina, Lakhina, A., Matta, I., Byers, J.: BRITE: Universal Topology Generation from a User’s PersGoogle Scholar
  14. 14.
    Schroeder, M.D., Birrel, A.D., Needham, R.M.: Experience with Grapevine: The Growth of a Distributed System. ACM Transactions of Computer Systems 2(1), 3–23 (1984)CrossRefGoogle Scholar
  15. 15.
    Golding, R.A.: Weak-Consistency Group Communication and Membership, PhD thesis, University of California, Santa Cruz, Comp. and Inf. Sciences Tech. Report UCSC-CRL-92-52 (December 1992)Google Scholar
  16. 16.
    Yook, S.-H., Jeong, H., Barabási, A.-L.: Modeling the internet’s large-scale topology. Tech. Report cond-mat/0107417, Cond. Matter Archive, (July 2001)Google Scholar
  17. 17.
    The Network Simulator,
  18. 18.
    Duvvuri, V., Shenoy, P., Tewari, R.: Adaptative Leases: A Strong Consistency Mechanism for the World Wide Web. In: IEEE INFOCOM 2000, pp. 834–843 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  1. 1.Universidad Autónoma de San Luis PotosíSan Luis PotosíMéxico

Personalised recommendations