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MonGNN: A neuroevolutionary-based solution for 5G network slices monitoring

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Abstract

Monitoring the status of network slices is a priority for network operators to ensure that SLAs are not violated. To overcome the limitations of direct slices' monitoring, network tomography (NT) is seen as a promising solution. NT-based solutions require constraining monitoring traffic to follow specific paths, which we can achieve by using segment-based routing (SR). This allows deploying customized probing scheme, such as cycles' probing. A major challenge with SR is, however, the limited length of the monitoring path. In this paper, we focus on the complexity of that task and propose MonGNN, a standalone solution based on Graph Neural Networks (GNNs) and genetic algorithms to find a trade-off between the quality of monitors' placement and the cost to achieve it. Simulation results show the efficiency of our approach compared to existing methods.
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Dates and versions

hal-03510074 , version 1 (04-01-2022)

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Anouar Rkhami, Yassine Hadjadj-Aoul, Gerardo Rubino, Abdelkader Outtagarts. MonGNN: A neuroevolutionary-based solution for 5G network slices monitoring. LCN 2021 - 46th IEEE Conference on Local Computer Networks, Oct 2021, Edmonton, Canada. pp.185-192, ⟨10.1109/LCN52139.2021.9524880⟩. ⟨hal-03510074⟩
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