Oak: User-Targeted Web Performance

Web performance has long proved to be one of the most sought after and difficult to achieve components for the web. Since the inception of the modern web infrastructure, the situation has been growing in complexity, adding remote hosts and objects, providing everything from computation infrastructure, content distribution capability, and targeted advertising. While many of these components provide improvements for some users, the complexity of the Internet often leaves other users suffering from poor performance. We propose Oak, a system which addresses client performance on the individual level, hence addressing challenges which may be unique to the user. Oak measures a user's performance for objects loading on a page, and determines which components are under-performing. Oak further provides an automated mechanism by which sites are able to replace resources with those provided by a better performing alternative service for a particular user. In this work, we demonstrate the prevalence of under-performing services on the web, finding that over 60% of the Alexa Top 500 have at least one under-preforming server. We further evaluate Oak on experimental and popular existing webpages, and demonstrate its effectiveness in making decisions in existing environments and with a distributed user base.