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
Refereed:
Yes
Uncontrolled Keywords:
Linear regression, Machine Learning, Multipath TCP, supervised learning, neural network
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 Silva
. Last Modified 09 Sep 2021 11:26