Predicting the effect of home Wi-Fi quality on QoE

International audiencePoor Wi-Fi quality can disrupt home users' internet experience, or the Quality of Experience (QoE). Detecting when Wi-Fi degrades QoE is extremely valuable for residential Internet Service Providers (ISPs) as home users often hold the ISP responsible whenever QoE degrades. Yet, ISPs have little visibility within the home to assist users. Our goal is to develop a system that runs on commodity access points (APs) to assist ISPs in detecting when Wi-Fi degrades QoE. Our first contribution is to develop a method to detect instances of poor QoE based on the passive observation of Wi-Fi quality metrics available in commodity APs (e.g., PHY rate). We use support vector regression to build predictors of QoE given Wi-Fi quality for popular internet applications. We then use K-means clustering to combine per-application predictors to identify regions of Wi-Fi quality where QoE is poor across applications. We call samples in these regions as poor QoE samples. Our second contribution is to apply our predictors to Wi-Fi metrics collected over one month from 3479 APs of customers of a large residential ISP. Our results show that QoE is good most of the time, still we find 11.6% of poor QoE samples. Worse, approximately 21% of stations have more than 25% poor QoE samples. In some cases, we estimate that Wi-Fi quality causes poor QoE for many hours, though in most cases poor QoE events are short

Predicting the effect of home Wi-Fi quality on QoE: Extended Technical Report

Poor Wi-Fi quality can disrupt home users' internet experience, or the Quality of Experience (QoE). Detecting when Wi-Fi degrades QoE is extremely valuable for residential Internet Service Providers (ISPs) as home users often hold the ISP responsible whenever QoE degrades. Yet, ISPs have little visibility within the home to assist users. Our goal is to develop a system that runs on commodity access points (APs) to assist ISPs in detecting when Wi-Fi degrades QoE. Our first contribution is to develop a method to detect instances of poor QoE based on the passive observation of Wi-Fi quality metrics available in commodity APs (e.g., PHY rate). We use support vector regression to build predictors of QoE given Wi-Fi quality for popular internet applications. We then use K-means clustering to combine per-application predictors to identify regions of Wi-Fi quality where QoE is poor across applications. We call samples in these regions as poor QoE samples. Our second contribution is to apply our predictors to Wi-Fi metrics collected over one month from 3479 APs of customers of a large residential ISP. Our results show that QoE is good most of the time, still we find 11.6% of poor QoE samples. Worse, approximately 21% of stations have more than 25% poor QoE samples. In some cases, we estimate that Wi-Fi quality causes poor QoE for many hours, though in most cases poor QoE events are short

Predicting the effect of home Wi-Fi quality on Web QoE

International audienceWi-Fi is the preferred way of accessing the Internet for many devices at home, but it is vulnerable to performance problems. The analysis of Wi-Fi quality metrics such as RSSI or PHY rate may indicate a number of problems, but users may not notice many of these problems if they don't degrade the performance of the applications they are using. In this work, we study the effects of the home Wi-Fi quality on Web browsing experience. We instrument a commodity access point (AP) to passively monitor Wi-Fi metrics and study the relationship between Wi-Fi metrics and Web QoE through controlled experiments in a Wi-Fi testbed. We use support vector regression to build a predictor of Web QoE when given Wi-Fi quality metrics available in most commercial APs. Our validation shows root-mean square errors on MOS predictions of 0.6432 in a controlled environment and of 0.9283 in our lab. We apply our predictor on Wi-Fi metrics collected in the wild from 4,880 APs to shed light on how Wi-Fi quality affects Web QoE in real homes

Passive Wi-Fi Link Capacity Estimation on Commodity Access Points

International audienceWi-Fi is the preferred way of accessing the internet for many devices at home, but it is vulnerable to performance problems. In this work, we propose a method to estimate the link capacity of a Wi-Fi link using physical layer metrics passively sampled on commodity access points. We build a model that predicts the maximum UDP throughput a device can sustain, which extends previous models to consider IEEE 802.11n optimizations such as frame aggregation. We validate our model through controlled experiments in an anechoic chamber. Over 95% of the link capacity predictions present errors below 5% when using our model with reference data. We show how link capacity estimation enables Wi-Fi diagnosis in two case studies where we predict the available bandwidth under microwave interference and in an office environment