Abstract
Understanding the quality of web browsing enjoyed by users is key to optimize services and keep users’ loyalty. This is crucial for both Content Providers and Internet Service Providers (ISPs). Quality is intrinsically subjective, and the complexity of today’s websites challenges its measurement. Objective metrics like OnLoad time and SpeedIndex are notable attempts to quantify web performance. However, these metrics can only be computed by instrumenting the browser and, thus, are not available to ISPs.PAIN (PAssive INdicator) is an automatic system to monitor the performance of websites from passive measurements. It is open source and available for download. It leverages only flow-level and DNS measurements which are still possible in the network despite the deployment of HTTPS. With unsupervised learning, PAIN automatically creates a model from the timeline of requests issued by browsers to render web pages, and uses it to measure website performance in real-time.We compare PAIN to objective metrics based on in-browser instrumentation and find strong correlations between the approaches. PAIN correctly highlights worsening network conditions and provides visibility into websites performance. We let PAIN run on an operational ISP network, and find that it is able to pinpoint performance variations across time and groups of users.
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