CAREER: Adaptive Web Execution: Supporting Billions of Diverse Users by Adapting Execution to Available Resources

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Web pages provide access to many critical services (e.g., health care, education, news), and are increasingly accessed by users with diverse network and device resources. Unfortunately, despite the fact that performance and functionality of page loads can vary drastically across resource settings, page loads today minimally adapt to their execution environments. This results in either underutilized resources or broken (incomplete) pages. This project aims to develop a new web paradigm called Adaptive Web Execution (AWE), in which page loads directly adapt their execution or content according to the available resources. The key goal is to maximize the performance and functionality that a web page can offer a user based on that user’s resource availability.The project involves three synergistic directions that develop the foundational algorithms and practical systems for realizing the AWE paradigm. First, it will develop strategies to collect and expose resource information to page loads in a way that balances cross-stack profiling overheads with actionable adaptation insights. Second, it will design a suite of content-preserving optimization systems that 1) dynamically adapt existing optimization strategies via efficient, web-focused machine learning, and 2) judiciously incorporate new, unused device resources into page loads. Third, it will create methods to simplify the development of content-altering adaptations, such as web-aware replay debugging that comprehensively evaluates page modifications across potential execution environments.This research will fundamentally transform and improve the web for multiple players. Users in developed regions will experience lower delays or increased functionality (increasing website revenue), while users in developing regions will get proper access to critical applications that are currently unusable with their available resources. AWE will also reduce development costs for websites and bridge the gap between web research and practical deployments by generalizing optimizations. The research will be informed by and evaluated in testbeds in developing regions, as well as through partnerships with industrial collaborators. The project will also involve 1) a new pedagogical 'full stack' approach to teaching networked system design, and 2) efforts to attract underrepresented undergraduate and K-12 students through the highly accessible lens of the web.The research artifacts and course materials designed as part of this project will be released on a public website: https://web.cs.ucla.edu/~ravi/awe/. In addition, the project site will include aggregate summaries of page structures and resource availability (in the form of privacy-preserving emulation traces) collected on developing region testbeds. The site will be regularly maintained, and project data will be kept for at least 5 years after publication, with extensions based on public interest.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.