Agreement in Epidemic Information Dissemination

Ayiad, Mosab M.; Katti, Amogh; Fatta, Giuseppe Di

doi:10.1007/978-3-319-45940-0_9

Cited by 4 publications

(6 citation statements)

References 14 publications

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“…The protocols are initialised by a peak data distribution whereas v i = 1, 0 < i ≤ N ; w 0 = 1 at seed node 0 and w i = 0, 1 < i ≤ N . Protocols' parameters are carefully adjusted to certain values to meet practical applications requirements [9]: the timeout value is set to T = 3 cycles, the tolerated error threshold is set to ε = 1%, the minimum number of consecutive cycles is set to Υ = 5 cycles, the length of the Q is set to 10 elements. On another hand, the protocol NCP is used as a peer sampling service and is configured to maintain a random k-regular overlay with k = 30.…”

Section: Simulations and Experimental Resultsmentioning

confidence: 99%

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Robust Epidemic Aggregation Under Churn

Ayiad

Fatta

2018

Internet and Distributed Computing Systems

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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.

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Section: Simulations and Experimental Resultsmentioning

confidence: 99%

“…assessing resource availability [8], parameter setting and network monitoring [4]. The protocol in [9] requires size estimation as an input parameter to achieve consensus.…”

Section: Epidemic Data Aggregation Processmentioning

confidence: 99%

Robust Epidemic Aggregation Under Churn

Ayiad

Fatta

2018

Internet and Distributed Computing Systems

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“…The tuple also contains leader for the computation of the election. In order for ECP to function, two other protocols are involved in the solution, System Size Estimation Protocol (SSEP ) [18] and Node Cache Protocol (NCP ) [19]. SSEP is an independent epidemic protocol that provides a robust estimate of system size and exports the size using the service getSize().…”

Section: Consensus In Epidemic Data Aggregationmentioning

confidence: 99%

“…The simulation model includes four protocols, ECP, PTP+ and two tree-based 3PC protocols. The protocol PTP+ is inspired by Phase Transition Protocol (PTP ) in [18]. PTP+ is adopted earlier in the formulation of this solution to achieve consensus in distributed data aggregation.…”

Section: Simulations Model and Configurationsmentioning

confidence: 99%

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Agreement in Epidemic Data Aggregation

Ayiad

Fatta

2017

2017 IEEE 23rd International Conference on Parallel and Distributed Systems (ICPADS)

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Computing and spreading global information in large-scale distributed systems pose significant challenges when scalability, parallelism, resilience and consistency are demanded. Epidemic protocols are a robust and scalable computing and communication paradigm that can be effectively used for information dissemination and data aggregation in a fully decentralised context where each network node requires the local computation of a global synopsis function. Theoretical analysis of epidemic protocols for synchronous and static network models provide guarantees on the convergence to a global target and on the consistency among the network nodes. However, practical applications in real-world networks may require the explicit detection of both local convergence and global agreement (consensus). This work introduces the Epidemic Consensus Protocol (ECP) for the determination of consensus on the convergence of a decentralised data aggregation task. ECP adopts a heuristic method to locally detect convergence of the aggregation task and stochastic phase transitions to detect global agreement and reach consensus. The performance of ECP has been investigated by means of simulations and compared to a tree-based Three-Phase Commit protocol (3PC). Although, as expected, ECP exhibits total communication costs greater than the optimal tree-based protocol, it is shown to have better performance and scalability properties; ECP can achieve faster convergence to consensus for large system sizes and inherits the intrinsic decentralisation, fault-tolerance and robustness properties of epidemic protocols.

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