Robust epidemic aggregation under churn

Ayiad, M. M. and Di Fatta, G. (2018) Robust epidemic aggregation under churn. In: The 10th International Conference on Internet and Distributed Computing System 2017 (IDCS 2017), 11 -13 December 2017, Mana Island Resort & Spa, Fiji, pp. 173-185.

Abstract/Summary

In large-scale distributed systems data aggregation is a fundamental task that provides a global synopsis over a distributed set of data values. Epidemic protocols are based on a randomised communication paradigm inspired by biological systems and have been proposed to provide decentralised, scalable and fault-tolerant solutions to the data aggregation problem. However, in epidemic aggregation, nodes failure and churn have a detrimental effect on the accuracy of the local estimates of the global aggregation target. In this paper, a novel approach, the Robust Epidemic Aggregation Protocol (REAP), is proposed to provide robustness in the presence of churn by detecting three distinct phases in the aggregation process. An analysis of the impact of each phase over the estimation accuracy is provided. In particular, a novel mechanism is introduced to improve the phase that is most critical for the protocol accuracy. REAP is validated by means of simulations and is shown to achieve convergence with a good level of accuracy for a reasonable range of node churn rates.

Additional Information	Part of the Lecture Notes in Computer Science book series (LNCS, volume 10794).
Item Type	Conference or Workshop Item (Paper)
URI	https://reading-clone.eprints-hosting.org/id/eprint/73501
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Additional Information	Part of the Lecture Notes in Computer Science book series (LNCS, volume 10794).
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