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Self-Stabilized Fast Gossiping Algorithms

Published:31 December 2015Publication History
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Abstract

In this article, we explore the topic of extending aggregate computation in distributed networks with self-stabilizing properties to withstand network dynamics. Existing research suggests that fast gossiping algorithms, based on the properties of order statistics applied to families of exponential random variables, are a viable solution for computing functions of the values stored in the network. We focus on the specific case in which network changes and failures occur in batches with a minimum frequency in the order of the diameter of the network. Our contribution consists in two self-stabilizing mechanisms, allowing fast gossiping algorithms to be applicable to dynamic networks with minor increase in resources usage. The resulting algorithms can be deployed in networks exhibiting churn, node stop-failures and resets, and random topological changes. The theoretical results are verified with simulations on synthetic data, showcasing desirable properties for large-scale network designers such as scalability, lack of single points of failure, and anonymity.

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    • Published in

      cover image ACM Transactions on Autonomous and Adaptive Systems
      ACM Transactions on Autonomous and Adaptive Systems  Volume 10, Issue 4
      Special Section on Best Papers from SEAMS 2014 and Regular Articles
      February 2016
      211 pages
      ISSN:1556-4665
      EISSN:1556-4703
      DOI:10.1145/2872308
      Issue’s Table of Contents

      Copyright © 2015 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 31 December 2015
      • Accepted: 1 August 2015
      • Revised: 1 June 2015
      • Received: 1 January 2015
      Published in taas Volume 10, Issue 4

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