Spanning Tree or Gossip for Aggregation: A Comparative Study

  • Lehel Nyers
  • Márk Jelasity
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8632)


Distributed aggregation queries like average and sum can be implemented in several different paradigms including gossip and hierarchical approaches. In the literature, these two paradigms are routinely associated with stereotypes such as “trees are fragile and complicated” and “gossip is slow and expensive”. However, a closer look reveals that these statements are not backed up by thorough studies. A fair and informative comparison is clearly needed. However, it is a very hard task, because the performance of protocols from the two paradigms depends on different subtleties of the environment and the implementation of the protocols. We tackle this problem by carefully designing the comparison study. We use state-of-the-art algorithms and propose the problem of monitoring the network size in the presence of churn as the ideal problem for comparing very different paradigms for global aggregation. Our experiments help us identify the most important factors that differentiate between gossip and spanning tree aggregation: the time needed to compute a truly global output, the properties of the underlying topology, and the sensitivity to dynamism. We demonstrate the effect of these factors in different practically interesting topologies and scenarios. Our results help us to choose the right protocol in the knowledge of the topology and dynamism patterns.


Span Tree Network Size Failure Detector Physical Topology Epoch Length 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Lehel Nyers
    • 1
    • 2
  • Márk Jelasity
    • 3
  1. 1.University of SzegedHungary
  2. 2.Subotica TechSuboticaSerbia
  3. 3.MTA-SZTE Research Group on Artificial IntelligenceUniversity of SzegedHungary

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