Adaptive Peer Sampling with Newscast

  • Norbert Tölgyesi
  • Márk Jelasity
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5704)


The peer sampling service is a middleware service that provides random samples from a large decentralized network to support gossip-based applications such as multicast, data aggregation and overlay topology management. Lightweight gossip-based implementations of the peer sampling service have been shown to provide good quality random sampling while also being extremely robust to many failure scenarios, including node churn and catastrophic failure. We identify two problems with these approaches. The first problem is related to message drop failures: if a node experiences a higher-than-average message drop rate then the probability of sampling this node in the network will decrease. The second problem is that the application layer at different nodes might request random samples at very different rates which can result in very poor random sampling especially at nodes with high request rates. We propose solutions for both problems. We focus on Newscast, a robust implementation of the peer sampling service. Our solution is based on simple extensions of the protocol and an adaptive self-control mechanism for its parameters, namely—without involving failure detectors—nodes passively monitor local protocol events using them as feedback for a local control loop for self-tuning the protocol parameters. The proposed solution is evaluated by simulation experiments.


Failure Detector Tabu List Drop Rate Failure Scenario Message Delay 
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 2009

Authors and Affiliations

  • Norbert Tölgyesi
    • 1
  • Márk Jelasity
    • 2
  1. 1.University of Szeged and Thot-Soft 2002 Kft.Hungary
  2. 2.University of Szeged and Hungarian Academy of SciencesHungary

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