Impact: an Unreliable Failure Detector Based on Processes' Relevance and the Confidence Degree in the System

Abstract : This technical report presents a new unreliable failure detector, called the Impact failure detector (FD) that, contrarily to the majority of traditional FDs, outputs a trust level value which expresses the degree of confidence in the system. An impact factor is assigned to each node and the trust level is equal to the sum of the impact factors of the nodes not suspected of failure. Moreover, a threshold parameter defines a lower bound value for the trust level, over which the confidence in the system is ensured. In particular, we defined a flexibility property that denotes the capacity of the Impact FD to tolerate a certain margin of failures or false suspicions, i.e., its capacity of considering different sets of responses that lead the system to trusted states. The Impact FD is suitable for systems that present node redundancy, heterogeneity of nodes, clustering feature, and allow a margin of failures which does not degrade the confidence in the system. The technical report also includes a timer-based distributed algorithm which implements a Impact FD, as well as its proof of correctness, for systems whose links are lossy asynchronous or for those whose all (or some) links are eventually timely. Performance evaluation results based on real PlanetLab traces confirm the degree of flexible applicability of our failure detector and, due to the accepted margin of failure, the both failures and false suspicions are more tolerated when compared to traditional unreliable failure detectors. We also show the equivalence of some classes of Impact FD in regard with Sigma and Omega classes, which are fundamental classes to circumvent the impossibility of consensus in asynchronous message-passing distributed systems.
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Rapport
[Research Report] Université Pierre et Marie Curie; INRIA Paris-Rocquencourt - Regal; Universidade Federal do Rio Grande do Sul. 2016
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Soumis le : jeudi 8 septembre 2016 - 17:26:10
Dernière modification le : jeudi 11 janvier 2018 - 06:28:03

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Anubis Rossetto, Luciana Arantes, Pierre Sens, Claudio Geyer. Impact: an Unreliable Failure Detector Based on Processes' Relevance and the Confidence Degree in the System. [Research Report] Université Pierre et Marie Curie; INRIA Paris-Rocquencourt - Regal; Universidade Federal do Rio Grande do Sul. 2016. 〈hal-01136595v3〉

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