Predicting User Dissatisfaction with Internet Application Performance at End-Hosts - Sorbonne Université Accéder directement au contenu
Communication Dans Un Congrès Année : 2013

Predicting User Dissatisfaction with Internet Application Performance at End-Hosts

Résumé

We design predictors of user dissatisfaction with the performance of applications that use networking. Our approach combines user-level feedback with low level machine and networking metrics. The main challenges of predicting user dissatisfaction, that arises when networking conditions adversely affect applications, comes from the scarcity of user feedback and the fact that poor performance episodes are rare. We develop a methodology to handle these challenges. Our method processes low level data via quantization and feature selection steps. We combine this with user labels and employ supervised learning techniques to build predictors. Using data from 19 personal machines, we show how to build training sets and demonstrate that non-linear SVMs achieve higher true positive rates (around 0.9) than predictors based on linear models. Finally we quantify the benefits of building per-application predictors as compared to general predictors that use data from multiple applications simultaneously to anticipate user dissatisfaction.
Fichier principal
Vignette du fichier
PredictingUsers-Infocom2013-Final.pdf (191.88 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00835032 , version 1 (17-06-2013)

Identifiants

Citer

Diana Zeaiter Joumblatt, Jaideep Chandrashekar, Branislav Kevton, Nina Taft, Renata Teixeira. Predicting User Dissatisfaction with Internet Application Performance at End-Hosts. IEEE INFOCOM (mini-conference), Apr 2013, Turin, Italy. pp.235-239, ⟨10.1109/INFCOM.2013.6566770⟩. ⟨hal-00835032⟩
282 Consultations
945 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More