Cognitive Network (CN) paradigm and Machine Learning (ML) techniques are increasingly becoming popular in Wireless network design. CN allows to efficiently arrange the network stack parameters among the nodes involved in the data transmission. It can be divided into two modules: the former permits to retrieve the ISO/OSI protocol stack parameters and the latter optimizes the transmission through a Cognitive Engine entity. This engine usually adopts ML algorithms to extract important features regarding the network, with the objective of either maximizing the global throughput or predicting relevant network behaviors. This paper analyzes how common ML algorithms are able to model transmissions occurring in a typical wireless network scenario. In particular, we test the algorithms with our CN testbed to predict transmission estimated time of arrivals (ETAs) in different network setups. We compare these results with those obtained via scp, which is the typical Unix/Linux shell program used to exchange files. We show that all learning techniques significantly improve the goodness of ETA prediction, thus suggesting to embed such algorithms in future scp revisions.

Applying Machine Learning Techniques to a Real Cognitive Network: File Transfer ETAs Prediction

DEL TESTA, DAVIDE;DANIELETTO, MATTEO;ZORZI, MICHELE
2015

Abstract

Cognitive Network (CN) paradigm and Machine Learning (ML) techniques are increasingly becoming popular in Wireless network design. CN allows to efficiently arrange the network stack parameters among the nodes involved in the data transmission. It can be divided into two modules: the former permits to retrieve the ISO/OSI protocol stack parameters and the latter optimizes the transmission through a Cognitive Engine entity. This engine usually adopts ML algorithms to extract important features regarding the network, with the objective of either maximizing the global throughput or predicting relevant network behaviors. This paper analyzes how common ML algorithms are able to model transmissions occurring in a typical wireless network scenario. In particular, we test the algorithms with our CN testbed to predict transmission estimated time of arrivals (ETAs) in different network setups. We compare these results with those obtained via scp, which is the typical Unix/Linux shell program used to exchange files. We show that all learning techniques significantly improve the goodness of ETA prediction, thus suggesting to embed such algorithms in future scp revisions.
Proceedings of the 2015 IEEE Global Communications Conference GLOBECOM
978-1-4799-5952-5
978-1-4799-5952-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3194854
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