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Preprints, Working Papers, ... Year : 2013

Predicting User Dissatisfaction with Application Performance on Home Gateways

Abstract

Home gateways connect devices in the home to the rest of the Internet. The gateway is ideally placed to adapt or control application performance to better fit the expectations of home users. However, it is not clear if we can infer user (dis)satisfaction with application performance with data available in a home gateway (i.e. mostly traffic metrics). Joumablatt et al. used machine learning methods to predict user (dis)satifaction with network application performance at end-hosts. In this paper, we study the feasibility of appying such predictors on a gateway. We show that we can measure the relevant metrics on a home gateway, we improve the proposed non-linear SVM predictor with parameter tuning, and we show that the gateway based predictor can achieve similar performance compared to the end-host predictor.
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Dates and versions

hal-00867282 , version 1 (27-09-2013)

Identifiers

  • HAL Id : hal-00867282 , version 1

Cite

Diego Neves da Hora, Maryam Najafabadi, Anna-Kaisa Pietilainen, Renata Teixeira. Predicting User Dissatisfaction with Application Performance on Home Gateways. 2013. ⟨hal-00867282⟩
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