Dynamic Traffic Prediction Model Retraining for Autonomous Network Operation

descriptionPublicationkeyboard_double_arrow_right Article , Conference object 02 Jul 2023 France, France, Spain Publisher:IEEEJournal:2023 23rd International Conference on Transparent Optical Networks (ICTON)Funded by:EC | DESIRE6G

Authors: Fatemehsadat Tabatabaeimehr; Luis Velasco 0001; Marc Ruiz 0001; Hamzeh Khalili; Ramon Aparicio-Pardo;

doi: 10.1109/icton59386.2023.10207375 , 10.5281/zenodo.8190218 , 10.5281/zenodo.7893103 , 10.5281/zenodo.8190219 , 10.5281/zenodo.7893102

handle: 2117/397981

Dynamic Traffic Prediction Model Retraining for Autonomous Network Operation

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Abstract

In general, the availability of an accurate machine learning (ML) model plays a particularly important role in the development of new networking solutions and is one of the main drivers for the development of 5G and beyond networking. Although an option is to update the model once inaccurate data is detected, such approach requires high computational effort, specially once the data history is large. In this paper, we propose an approach that combines a traffic prediction model based on Long Short-Term Memory (LSTM) with an analysis module for dynamic connection capacity allocation. Once the model is generated, re-training can be triggered after inaccuracies are detected by the analysis module. Illustrative numerical results show the benefits from the proposed decision-based re-training approach to reduce the number of re-training rounds while maintaining model accuracy.

Countries

France, France, Spain

Related Organizations

Universitat Polite`cnica de Catalunya
Spain
UNIVERSITE COTE D'AZUR
Universitat Polit?cnica de Catalunya
Spain
Université Côte d'Azur
France
Université Côte d'Azur
France

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Keywords

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Autonomous networks, Real-time data processing, Telecomunicació -- Tràfic -- Gestió, 5G mobile communication systems, Autonomous operations, autonomous operation, Machine learning, Aprenentatge automàtic, Long short-term memory, Metro network, metro network, model retraining, traffic prediction, Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telecomunicació òptica, Subways, [INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI], Network operations, Metro networks, Telecommunication -- Traffic -- Management, autonomous operation metro network traffic prediction model retraining LSTM, autonomous network operation, Traffic prediction, Dynamic traffic, Model retraining, Computational effort, Machine learning models, Autonomous operation, LSTM, Traffic prediction model, Temps real (Informàtica), Forecasting

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