Efficient Gossip and Robust Distributed Computation

  • Chryssis Georgiou
  • Dariusz R. Kowalski
  • Alex A. Shvartsman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2848)


This paper presents an efficient deterministic gossip algorithm for p synchronous, crash-prone, message-passing processors. The algorithm has time complexity T = O(log2 p) and message complexity M=O(p 1 + ε), for any ε>0. This substantially improves the message complexity of the previous best algorithm that has M=O(p 1.77), while maintaining the same time complexity. The strength of the new algorithm is demonstrated by constructing a deterministic algorithm for performing n tasks in this distributed setting. Previous solutions used coordinator or check-pointing approaches, immediately incurring a work penalty Ω(n + f.p) for f crashes, or relied on strong communication primitives, such as reliable broadcast, or had work too close to the trivial Θ(p.n) bound of oblivious algorithms.The new algorithm uses p crash-prone processors to perform n similar and idempotent tasks so long as one processor remains active. The work of the algorithm is W = O(n + p.min{f + 1,log 3 p}) and its message complexity is M = O(fp ε  + pmin{f + 1, logp}), for any ε>0. This substantially improves the work complexity of previous solutions using simple point-to-point messaging, while “meeting or beating” the corresponding message complexity bounds. The new algorithms use communication graphs and permutations with certain combinatorial properties that are shown to exist. The algorithms are correct for any permutations, and in particular, the same expected bounds can be achieved using random permutations.


Time Complexity Failure Pattern Communication Graph Message Complexity Work Complexity 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Chryssis Georgiou
    • 1
  • Dariusz R. Kowalski
    • 1
    • 2
  • Alex A. Shvartsman
    • 1
    • 3
  1. 1.Department of Computer Science and EngineeringUniversity of ConnecticutStorrsUSA
  2. 2.Instytut InformatykiUniwersytet WarszawskiWarsawPoland
  3. 3.Computer Science and Artificial Intelligence LaboratoryMassachusetts Institute of TechnologyCambridgeUSA

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