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An adaptive restart mechanism for continuous epidemic systems

Ayiad, M. M. and Di Fatta, G. (2019) An adaptive restart mechanism for continuous epidemic systems. In: International Conference on Internet and Distributed Computing Systems, 10-12 October, Naples, Italy, pp. 57-68,

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To link to this item DOI: 10.1007/978-3-030-34914-1_6


Software services based on large-scale distributed systems demand continuous and decentralised solutions for achieving system consistency and providing operational monitoring. Epidemic data aggregation algorithms provide decentralised, scalable and fault-tolerant solutions that can be used for system-wide tasks such as global state determination, monitoring and consensus. Existing continuous epidemic algorithms either periodically restart at fixed epochs or apply changes in the system state instantly producing less accurate approximation. This work introduces an innovative mechanism without fixed epochs that monitors the system state and restarts upon the detection of the system convergence or divergence. The mechanism makes correct aggregation with an approximation error as small as desired. The proposed solution is validated and analysed by means of simulations under static and dynamic network conditions.

Item Type:Conference or Workshop Item (Paper)
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:90173


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