An innovative machine learning approach to improve MPTCP performance

Silva, Fábio; Togou, Mohammed Amine; Muntean, Gabriel-Miro

An innovative machine learning approach to improve MPTCP performance

Files

Silva_Fabio_-_Paper_Id_2_-_HPCS2020_MCWN2020_(1).pdf(535.98 KB)

Accepted Version

Date

2020-07

Authors

Silva, Fábio

Togou, Mohammed Amine

Muntean, Gabriel-Miro

Abstract

This paper presents, describes and evaluates the Machine Learning Performance Monitor (MLPM), an innovative Machine Learning (ML) approach to fore cast 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.

Keywords

Linear regression , Machine learning , Multipath TCP , Supervised learning , Neural network

Citation

Silva, F., Togou, M. A. and Muntean, G.-M. (2020) ‘An innovative machine learning approach to improve MPTCP performance', 2020 International Conference on High Performance Computing and Simulation (HPCS 2020), Barcelona, Spain, 20-24 July.