Predicting file downloading time in cellular network: Large-Scale analysis of machine learning approaches

Alassane Samba 1, 2, 3 Yann Busnel 4, 2, 5 Alberto Blanc 1, 2 Philippe Dooze 3 Gwendal Simon 1, 2
1 ADOPNET - Advanced technologies for operated networks
UR1 - Université de Rennes 1, IMT Atlantique - IMT Atlantique Bretagne-Pays de la Loire, IRISA-D2 - RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES
5 DIONYSOS - Dependability Interoperability and perfOrmance aNalYsiS Of networkS
Inria Rennes – Bretagne Atlantique , IRISA-D2 - RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES
Abstract : Downlink data rates can vary significantly in cellular networks, with a potentially non-negligible effect on the user experience. Content providers address this problem by using different representations (e.g. , picture resolution, video resolution and rate) of the same content and switch among these based on measurements collected during the connection. Knowing the achievable data rate before the connection establishment should definitely help content providers to choose the most appropriate representation from the very beginning. We have conducted several large measurement campaigns involving a panel of users connected to a production network in France, to determine whether it is possible to predict the achievable data rate using measurements collected, before establishing the connection to the content provider, on the operator's network and on the mobile node. We establish evidence that it is indeed possible to exploit these measurements to predict, with an acceptable accuracy, the achievable data rate. We thus introduce cooperation strategies between the mobile user, the network operator and the content provider to implement such anticipatory solution.
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Submitted on : Wednesday, December 12, 2018 - 11:05:39 AM
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Alassane Samba, Yann Busnel, Alberto Blanc, Philippe Dooze, Gwendal Simon. Predicting file downloading time in cellular network: Large-Scale analysis of machine learning approaches. Computer Networks, Elsevier, 2018, 145, pp.243-254. ⟨10.1016/j.comnet.2018.09.002⟩. ⟨hal-01951758⟩

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