On Leveraging Browser-Level Measurements for Network Troubleshooting

Despite advances in network technologies, slow web browsing remains a persistent issue, and its troubleshooting continues to be challenging. To address this challenge, our work presents a lightweight passive measurement solution that relies on web performance data readily available within the browser, such as the connect time, the response time, and the page load time, to infer network performance, detect anomalies, and troubleshoot their origins. Through controlled network experiments with manually injected anomalies, including multiple concurrent performance issues, and leveraging a dataset of more than 43, 000 webpages and tens of thousands of network scenarios, we develop a predictive model using machine learning that is capable of estimating network performance metrics with acceptable accuracy. By solely relying on users' daily web activity, our solution can continuously monitor network performance, identify anomalies, and provide actionable insights, without overloading the network with measurement probes, thus making network troubleshooting accessible and non-intrusive for everyday users.