PAIN

Abstract

Understanding the quality of web browsing enjoyed by users is key to optimize services and keep users' loyalty. This is crucial for Internet Service Providers (ISPs) to anticipate problems. Quality is subjective, and the complexity of today's pages challenges its measurement. OnLoad time and SpeedIndex are notable attempts to quantify web performance. However, these metrics are computed using browser instrumentation and, thus, are not available to ISPs. PAIN (PAssive INdicator) is an automatic system to observe the performance of web pages at ISPs. It leverages passive flow-level and DNS measurements which are still available 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 analyze the web performance in real-time. We compare PAIN to indicators based on in-browser instrumentation and find strong correlations between the approaches. It reflects worsening network conditions and provides visibility into web performance for ISPs.