Web Performance Automation for The People

Abstract

Web performance is important for the user experience and can heavily influence web page revenues. While there are many established Web Performance Optimization (WPO) methods, our work so far has clearly shown that new network protocols, optimized browsers and cutting-edge web standards can have a significant impact on known best practices. Additionally, there is still low-hanging fruit to be exploited, in the form of personalizing performance based on user context (i.e., current device, network, browser) and user preferences (e.g., text reading vs multimedia experience). In our PhD project, we strive to integrate this user-specific metadata into dynamic configurations for both existing and new automated WPO techniques. An intermediate server can (pre)generate optimized versions of a web page, which are then selected based on user context and preferences. Additional metadata is also passed along to the browser, enabling improvements on that side, and used to steer new network protocols to speed up the incremental delivery of page resources. We use the Speeder platform to perform and evaluate full-factorial objective measurements and use subjective user studies across a range of groups to assess the applicability of our methods to end users. Our aim is to provide insights in how WPO can be tweaked for specific users, in the hopes of leading to new web standards that enable this behavior.