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

职业：自适应 Web 执行：通过使执行适应可用资源来支持数十亿不同的用户

• 批准号: 2152313
• 负责人: Ravi Netravali
• 金额: $ 49.98万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2152313&HistoricalAwards=false
• 关键词: CAREER; Adaptive; Web; Execution; Supporting

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.

网页提供对许多关键服务（例如医疗保健、教育、新闻）的访问，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存在巨大差异。 ，当今的页面加载很少适应其执行环境，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称为自适应 Web 执行（AWE）的新 Web 范例，其中页面加载直接适应其执行或。内容根据关键目标是根据用户的资源可用性最大化网页可以为用户提供的性能和功能。该项目涉及三个协同方向，开发用于实现 AWE 范例的基础算法和实用系统。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见解。 其次，它将设计一套内容保留优化系统，1）通过以下方式动态调整现有的优化策略。高效的，以网络为中心的机器学习，2）明智地将新的、未使用的设备资源合并到页面加载中，第三，它将创建简化内容更改适应的开发的方法，例如全面评估潜在页面修改的网络感知重放调试。这项研究将从根本上改变和改进多个参与者的网络，发达地区的用户将体验到更低的延迟或增加的功能（增加网站收入），而发展中地区的用户将能够正确访问当前无法使用的关键应用程序。 AWE 还将利用其现有资源，通过推广优化来降低网站的开发成本，并弥合网络研究和实际部署之间的差距。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) 通过高度可访问的网络镜头吸引代表性不足的本科生和 K-12 学生。研究成果和课程材料作为该项目一部分的设计将在公共网站上发布：https://web.cs.ucla.edu/~ravi/awe/ 此外，该项目网站将包括页面结构和资源可用性的汇总摘要（在在发展中地区测试平台上收集的隐私保护仿真痕迹的形式）将定期维护，项目数据将在发布后保留至少 5 年，并根据公共利益进行延期。该奖项反映了 NSF 的法定使命和使命。已被视为值得通过使用基金会的智力优势和更广泛的影响审查标准进行评估来获得支持。

项目成果

Marauder: Synergized Caching and Prefetching for Low-Risk Mobile App Acceleration

Marauder：协同缓存和预取以实现低风险移动应用程序加速

• DOI: 10.1145/3458864.3466866
• 发表时间: 2021-06
• 期刊: and Services
• 作者: Ramanujam, Murali;Madhyastha, Harsha V.;Netravali, Ravi
• 通讯作者: Netravali, Ravi

Rethinking Client-Side Caching for the Mobile Web

重新思考移动网络的客户端缓存

• DOI: 10.1145/3446382.3448664
• 发表时间: 2021-02-24
• 期刊: Proceedings of the 22nd International Workshop on Mobile Computing Systems and Applications
• 作者: Ayush Goel;Vaspol Ruamviboonsuk;R. Netravali;H. Madhyastha
• 通讯作者: H. Madhyastha

Horcrux: Automatic JavaScript Parallelism for Resource-Efficient Web Computation

Horcrux：自动 JavaScript 并行，实现资源高效的 Web 计算

• 发表时间: 2021-07
• 期刊: 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI
• 作者: Mardani, Shaghayegh;Goel, Ayush;Ko, Ronny;Madhyastha, Harsha;Netravali, Ravi
• 通讯作者: Netravali, Ravi

Jawa: Web Archival in the Era of JavaScript

Jawa：JavaScript 时代的网络档案

• 发表时间: 2022-07
• 期刊: 16th USENIX Symposium on Operating Systems Design and Implementation (OSDI 22
• 作者: Goel, Ayush;Netravali, Ravi;Madhyastha, Harsha
• 通讯作者: Madhyastha, Harsha

Floo: automatic, lightweight memoization for faster mobile apps

Floo：自动、轻量级记忆，可实现更快的移动应用程序

• DOI: 10.1145/3498361.3538929
• 发表时间: 2022-06
• 期刊: and Services
• 作者: Ramanujam, Murali;Chen, Helen;Mardani, Shaghayegh;Netravali, Ravi
• 通讯作者: Netravali, Ravi