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An innovative machine learning approach to improve MPTCP performance

Silva, Fabio orcid logoORCID: 0000-0001-6019-372X, Togou, Mohammed orcid logoORCID: 0000-0002-8374-910X and Muntean, Gabriel-Miro orcid logoORCID: 0000-0002-9332-4770 (2020) An innovative machine learning approach to improve MPTCP performance. In: The 18th International Conference on High Performance Computing & Simulation (HPCS 2020), 22 - 27 March 2021, Barcelona, Spain (Online).

This paper presents, describes and evaluates the Machine Learning Performance Monitor (MLPM), an innovative Machine Learning (ML) approach to forecast and extrapolate the performance of several network features (e.g., latency, throughput) in a Multipath TCP (MPTCP) subflow pool. MLPM uses linear regression to predict the performance of network features along with Artificial Neural Network linear classifier to choose the best subflow (i.e., network path) capable of delivering the best performance to a given set of the network features. Results show that MLPM delivers better performance in terms of throughput and latency compared to existing schemes as it improves the MPTCP scheduler performance.
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Uncontrolled Keywords:Linear regression, Machine Learning, Multipath TCP, supervised learning, neural network
Subjects:Computer Science > Computer networks
Computer Science > Machine learning
Engineering > Virtual reality
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Published in: IEEE Transactions on Intelligent Transportation Systems. . IEEE.
Official URL:https://dx.doi.org/10.1109/TITS.2021.3052840
Copyright Information:© 2020 The Authors
Funders:European Union’s Horizon 2020 Research and Innovation Programme under grants 688503 and 870610, Science Foundation Ireland grants 13/RC/2094 (Lero), 16/SP/3804 (ENABLE), 12/RC/2289_P2 (Insight)
ID Code:25963
Deposited On:08 Jun 2021 11:14 by Fabio Rodrigues Da silva . Last Modified 09 Sep 2021 11:26

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