Improving Gossip Dynamics Through Overlapping Replicates

  • Danilo PianiniEmail author
  • Jacob Beal
  • Mirko Viroli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9686)


Gossip protocols are a fast and effective strategy for computing a wide class of aggregate functions involving coordination of large sets of nodes. The monotonic nature of gossip protocols, however, mean that they can typically only adjust their estimate in one direction unless restarted, which disrupts the values being returned. We propose to improve the dynamical performance of gossip by running multiple replicates of a gossip algorithm, overlapping in time. We find that this approach can significantly reduce the error of aggregate function estimates compared to both typical gossip implementations and tree-based estimation functions.


Aggregate Function Prior Method Network Diameter Algorithm Instance Circular Arena 
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.


  1. 1.
    Beal, J.: Accelerating approximate consensus with self-organizing overlays. In: Spatial Computing Workshop, May 2013Google Scholar
  2. 2.
    Beal, J.: Superdiffusive dispersion and mixing of swarms. ACM Trans. Auton. Adapt. Syst. 10(2), 1–24 (2015)CrossRefGoogle Scholar
  3. 3.
    Beal, J., Pianini, D., Viroli, M.: Aggregate programming for the internet of things. IEEE Comput. 48(9), 22–30 (2015)CrossRefGoogle Scholar
  4. 4.
    Beal, J., Viroli, M.: Building blocks for aggregate programming of self-organising applications. In: IEEE SASO Workshops, pp. 8–13 (2014)Google Scholar
  5. 5.
    Birman, K.: The promise, and limitations, of gossip protocols. ACM SIGOPS Oper. Syst. Rev. 41(5), 8–13 (2007)CrossRefGoogle Scholar
  6. 6.
    Chandra, T.D., Griesemer, R., Redstone, J.: Paxos made live: an engineering perspective. In: Principles of Distributed Computing, pp. 398–407 (2007)Google Scholar
  7. 7.
    Damiani, F., Viroli, M., Beal, J.: A type-sound calculus of computational fields. Sci. Comput. Program. 117, 17–44 (2016)CrossRefGoogle Scholar
  8. 8.
    Damiani, F., Viroli, M., Pianini, D., Beal, J.: Code mobility meets self-organisation: a higher-order calculus of computational fields. In: Graf, S., Viswanathan, M. (eds.) Formal Techniques for Distributed Objects, Components, and Systems. LNCS, vol. 9039, pp. 113–128. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  9. 9.
    Elhage, N., Beal, J.: Laplacian-based consensus on spatial computers. In: International Conference on Autonomous Agents and Multiagent Systems, pp. 907–914 (2010)Google Scholar
  10. 10.
    Fischer, M.J., Lynch, N.A., Paterson, M.S.: Impossibility of distributed consensus with one faulty process. J. ACM (JACM) 32(2), 374–382 (1985)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Gupta, I., Birman, K., Linga, P., Demers, A., van Renesse, R.: Kelips: building an efficient and stable P2P DHT through increased memory and background overhead. In: Kaashoek, M.F., Stoica, I. (eds.) Peer-to-Peer Systems II. LNCS, vol. 2735, pp. 160–169. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  12. 12.
    Jelasity, M., Montresor, A., Babaoglu, O.: Gossip-based aggregation in large dynamic networks. ACM Trans. Comput. Syst. (TOCS) 23(3), 219–252 (2005)CrossRefGoogle Scholar
  13. 13.
    Jelasity, M., Montresor, A., Babaoglu, O.: T-man: gossip-based fast overlay topology construction. Comput. Netw. 53(13), 2321–2339 (2009)CrossRefzbMATHGoogle Scholar
  14. 14.
    Lamport, L.: The part-time parliament. ACM Trans. Comput. Syst. 16(2), 133–169 (1998)CrossRefGoogle Scholar
  15. 15.
    Lynch, N.: Distributed Algorithms. Morgan Kaufmann, San Francisco (1996)zbMATHGoogle Scholar
  16. 16.
    Mosk-Aoyama, D., Shah, D.: Fast distributed algorithms for computing separable functions. IEEE Trans. Inf. Theor. 54(7), 2997–3007 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Olfati-Saber, R., Fax, J.A., Murray, R.M.: Consensus and cooperation in networked multi-agent systems. Proc. IEEE 95(1), 215–233 (2007)CrossRefGoogle Scholar
  18. 18.
    Perlman, R.J.: Fault-tolerant broadcast of routing information. Comput. Netw. 7, 395–405 (1983). Google Scholar
  19. 19.
    Pianini, D., Montagna, S., Viroli, M.: Chemical-oriented simulation of computational systems with ALCHEMIST. J. Simul. 7(3), 202–215 (2013)CrossRefGoogle Scholar
  20. 20.
    Pianini, D., Viroli, M., Beal, J.: Protelis: practical aggregate programming. In: ACM Symposium on Applied Computing, pp. 1846–1853 (2015)Google Scholar
  21. 21.
    Shah, D.: Gossip Algorithms. Now Publishers Inc, Norwell (2009)zbMATHGoogle Scholar
  22. 22.
    Viroli, M., Beal, J., Damiani, F., Pianini, D.: Efficient engineering of complex self-organising systems by self-stabilising fields. In: IEEE SASO, pp. 81–90 (2015)Google Scholar
  23. 23.
    Voulgaris, S., van Steen, M.: An epidemic protocol for managing routing tables in very large peer-to-peer networks. In: Brunner, M., Keller, A. (eds.) DSOM 2003. LNCS, vol. 2867, pp. 41–54. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  24. 24.
    Zambonelli, F.: Toward sociotechnical urban superorganisms. IEEE Comput. 45(8), 76–78 (2012)CrossRefGoogle Scholar

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© IFIP International Federation for Information Processing 2016

Authors and Affiliations

  1. 1.Alma Mater Studiorum–Università di BolognaCesenaItaly
  2. 2.Raytheon BBN TechnologiesCambridgeUSA

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