What-If Analysis of Page Load Time in Web Browsers Using Causal Profiling

Web browsers have become one of the most commonly used applications for desktop and mobile users. Despite recent advances in network speeds and several techniques to speed up web page loading, browsers still suffer from relatively long page load time (PLT). Particularly, web applications need browsers to have a higher performance to compete with native applications. Recent studies show that network connection is not the bottleneck of the browser's performance anymore, however, no subsequent analysis has been conducted to inspect which parts of the browser's computation contribute to the performance overhead. In this paper, we apply a comprehensive and quantitative what-if analysis on the web browser's page loading process. Unlike conventional profiling methods, we apply causal profiling to precisely determine the impact of each computation stage such as HTML parsing and Layout on PLT. For this purpose, we develop COZ+1, a high-performance causal profiler capable of analyzing large software systems such as the Chromium browser. COZ+ highlights the most influential spots for further optimization. For example, it shows that optimizing JavaScript by 40% is expected to improve the Chromium page loading performance by more than 8.5% under typical network conditions.