Abstract
Telecommunication networks are designed to route data along fixed pathways, and so have minimal reactivity to emergent loads. To service today’s increased data requirements, networks management must be revolutionised so as to proactively respond to anomalies quickly and efficiently. To equip the network with resilience, a distributed design calls for node agency, so that nodes can predict the emergence of critical data loads leading to disruptions. This is to inform prognostics models and proactive maintenance planning. Proactive maintenance needs KPIs, most importantly probability and impact of failure, estimated by criticality which is the negative impact on connectedness in a network resulting from removing some element. In this paper, we studied criticality in the sense of increased incidence of data congestion caused by a node being unable to process new data packets. We introduce three novel, distributed measures of criticality which can be used to predict the behaviour of dynamic processes occurring on a network. Their performance is compared and tested on a simulated diffusive data transfer network. The results show potential for the distributed dynamic criticality measures to predict the accumulation of data packet loads within a communications network. These measures are predicted to be useful in proactive maintenance and routing for telecommunications, as well as informing businesses of partner criticality in supply networks.
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Acknowledgements
This research was supported by the EPSRC and BT Prosperity Partnership project: Next Generation Converged Digital Infrastructure, grant number EP/R004935/1, and the UK Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Partnership Award for the University of Cambridge, grant number EP/R513180/1.
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Proselkov, Y., Herrera, M., Parlikad, A.K., Brintrup, A. (2021). Distributed Dynamic Measures of Criticality for Telecommunication Networks. In: Borangiu, T., Trentesaux, D., Leitão, P., Cardin, O., Lamouri, S. (eds) Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future. SOHOMA 2020. Studies in Computational Intelligence, vol 952. Springer, Cham. https://doi.org/10.1007/978-3-030-69373-2_30
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DOI: https://doi.org/10.1007/978-3-030-69373-2_30
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