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

网页提供对许多关键服务的访问（例如，保健、教育、新闻），并且越来越多地被具有不同网络和设备资源的用户访问。不幸的是，尽管页面加载的性能和功能在不同的资源设置中可能会有很大的差异，但今天的页面加载很少能适应它们的执行环境。这会导致资源利用不足或页面损坏（不完整）。该项目旨在开发一种新的Web范例，称为自适应Web执行（AWE），其中页面加载直接根据可用资源调整其执行或内容。主要目标是最大限度地提高性能和功能，网页可以提供用户的基础上，用户的资源availability.The项目涉及三个协同方向，开发的基本算法和实用系统，实现AWE范式。首先，它将制定策略来收集资源信息并将其公开给页面加载，以平衡跨堆栈分析开销和可操作的适应性见解。其次，它将设计一套内容保留优化系统，1）通过高效的、以Web为中心的机器学习动态地适应现有的优化策略，2）明智地将新的、未使用的设备资源整合到页面加载中。第三，它将创建方法来简化内容改变适配的开发，例如Web感知重放调试，该调试全面评估潜在执行环境中的页面修改。发达区域的用户将减少延迟或增加功能（增加网站收入），而发展中区域的用户将能够适当访问目前无法利用其现有资源的关键应用程序。AWE还将降低网站的开发成本，并通过推广优化弥合Web研究和实际部署之间的差距。该研究将通过发展中地区的试验台以及与工业合作者的伙伴关系提供信息并进行评估。该项目还将涉及1）一种新的教学“全栈”方法来教授网络系统设计，以及2）通过网络的高度可访问的透镜来吸引代表性不足的本科生和K-12学生的努力。作为该项目一部分设计的研究工件和课程材料将在公共网站上发布：https://web.cs.ucla.edu/~ravi/awe/。此外，该项目网站将包括在发展中地区试验平台上收集的页面结构和资源可用性的汇总摘要（以隐私保护模拟痕迹的形式）。该网站将定期维护，项目数据将在出版后至少保留5年，并根据公众利益延长。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.