533 research outputs found
Fault-Tolerant Aggregation: Flow-Updating Meets Mass-Distribution
Flow-Updating (FU) is a fault-tolerant technique that has proved to be
efficient in practice for the distributed computation of aggregate functions in
communication networks where individual processors do not have access to global
information. Previous distributed aggregation protocols, based on repeated
sharing of input values (or mass) among processors, sometimes called
Mass-Distribution (MD) protocols, are not resilient to communication failures
(or message loss) because such failures yield a loss of mass. In this paper, we
present a protocol which we call Mass-Distribution with Flow-Updating (MDFU).
We obtain MDFU by applying FU techniques to classic MD. We analyze the
convergence time of MDFU showing that stochastic message loss produces low
overhead. This is the first convergence proof of an FU-based algorithm. We
evaluate MDFU experimentally, comparing it with previous MD and FU protocols,
and verifying the behavior predicted by the analysis. Finally, given that MDFU
incurs a fixed deviation proportional to the message-loss rate, we adjust the
accuracy of MDFU heuristically in a new protocol called MDFU with Linear
Prediction (MDFU-LP). The evaluation shows that both MDFU and MDFU-LP behave
very well in practice, even under high rates of message loss and even changing
the input values dynamically.Comment: 18 pages, 5 figures, To appear in OPODIS 201
Hybrid Dissemination: Adding Determinism to Probabilistic Multicasting in Large-Scale P2P Systems
Abstract. Epidemic protocols have demonstrated remarkable scalability and robustness in disseminating information on internet-scale, dynamic P2P systems. However, popular instances of such protocols suffer from a number of significant drawbacks, such as increased message overhead in push-based systems, or low dissemination speed in pull-based ones. In this paper we study push-based epidemic dissemination algorithms, in terms of hit ratio, communication overhead, dissemination speed, and resilience to failures and node churn. We devise a hybrid push-based dissemination algorithm, combining probabilistic with deterministic properties, which limits message overhead to an order of magnitude lower than that of the purely probabilistic dissemination model, while retaining strong probabilistic guarantees for complete dissemination of messages. Our extensive experimentation shows that our proposed algorithm outperforms that model both in static and dynamic network scenarios, as well as in the face of large-scale catastrophic failures. Moreover, the proposed algorithm distributes the dissemination load uniformly on all participating nodes. Keywords: Epidemic/Gossip protocols, Information Dissemination, Peer-to-Peer
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