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

Rkhami, Anouar; Hadjadj‐Aoul, Yassine; Rubino, Gerardo; Outtagarts, Abdelkader

doi:10.1109/lcn52139.2021.9524880

Cited by 2 publications

(1 citation statement)

References 18 publications

(14 reference statements)

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“…In that sense, RouteNet [9] is a GNN model that can directly learn from graph-like data, including network topologies, routings, and offered traffic, to predict network KPIs. In recent research, other GNN solutions have been applied to solve autonomous network management [25], network slicing monitoring [26], network slicing control [27], or Software Defined Networking (SDN) end-to-end delay prediction [28]. However, GNNs, as ML algorithms, are sensitive to the mismatch between training data and testing data, i.e., training and testing data do not come from the same distribution.…”

Section: Problem Contextmentioning

confidence: 99%

Improving network delay predictions using GNNs

Farreras

Soto

Camelo

et al. 2022

Preprint

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Autonomous network management is crucial for Fifth Generation (5G) and Beyond 5G (B5G) networks, where constant environmental changes must be predicted, adapting network configurations accordingly. Model-ing tools are required to predict the impact on performance when new traffic combinations and routings are applied in the network. Mathematical analysis and network simulators are techniques to model modern networks but have limitations of low accuracy or high execution times, respectively. To overcome these limitations, Machine Learning (ML) algorithms, and more concretely, Graph Neural Networks (GNNs), are proposed to model modern networks due to their ability to capture complex relationships from graph-like data while predicting network properties with high accuracy. However, one of the main issues when using GNNs is that they cannot generalize to larger networks, when different input data (i.e. smaller network topologies, and shorter paths) are provided for training and predictions (e.g., larger network topolo-gies, larger paths). This paper addresses the GNN generalization problem by leveraging networking concepts and analysis to improve a base-line model. The results applied on the RouteNet baseline developed by Barcelona Neural Networking Center-Universitat Politècnica de Catalunya (BNN-UPC) significantly improve the model’s accuracy in larger graphs. In addition, we propose a closed-loop control context where the resulting GNN model could be potentially used in different use cases.

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Section: Problem Contextmentioning

confidence: 99%

Improving network delay predictions using GNNs

Farreras

Soto

Camelo

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

Improving Network Delay Predictions Using GNNs

et al. 2023

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Autonomous network management is crucial for Fifth Generation (5G) and Beyond 5G (B5G) networks, where a constantly changing environment is expected and network configuration must adapt accordingly. Modeling tools are required to predict the impact on performance (packet and delay loss) when new traffic demands arrives and when changes in routing paths are applied in the network. Mathematical analysis and network simulators are techniques for modeling networks but both have limitations, as the former provides low accuracy and the latter requires high execution times. To overcome these limitations, machine learning (ML) algorithms, and more specifically, graph neural networks (GNNs), are proposed for network modeling due to their ability to capture complex relationships from graph-like data while predicting network properties with high accuracy and low computational requirements. However, one of the main issues when using GNNs is their lack of generalization capability to larger networks, i.e., when trained in small networks (in number of nodes, paths length, links capacity), the accuracy of predictions on larger networks is poor. This paper addresses the GNN generalization problem by the use of fundamental networking concepts. Our solution is built from a baseline GNN model called RouteNet (developed by Barcelona Neural Networking Center-Universitat Politècnica de Catalunya (BNN-UPC)) that predicts the average delay in network paths, and through a number of simple additions significantly improves the prediction accuracy in larger networks. The improvement ratio compared to the baseline model is 101, from a 187.28% to a 1.828%, measured by the Mean Average Percentage Error (MAPE). In addition, we propose a closed-loop control context where the resulting GNN model could be potentially used in different use cases.

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

Cited by 2 publications

References 18 publications

Improving network delay predictions using GNNs

Improving network delay predictions using GNNs

Improving Network Delay Predictions Using GNNs

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