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: 1943621
• 负责人: Ravi Netravali
• 金额: $ 49.98万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2021-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1943621&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.

网页提供了对许多关键服务（例如医疗保健，教育，新闻）的访问权限，并越来越多地由使用潜水员网络和设备资源的用户访问。不幸的是，使命载荷的性能和功能可以在资源设置之间发生巨大变化，页面加载今天最少适应其执行环境。这会导致资源未充分利用，要么破坏（不完整）页面。该项目旨在开发一种称为自适应Web执行（AWE）的新的Web范式，其中页面可以根据可用资源直接加载其执行或内容。关键目标是最大化网页可以根据用户资源可用性提供用户的性能和功能。该项目涉及三个协同方向，这些方向开发了实现敬畏范式的基础算法和实用系统。首先，它将制定策略，以将交叉堆栈概况的开销与可行的适应性见解之间的方式收集和公开资源信息到页面负载。其次，它将设计一套具有内容的优化系统，1）通过高效，以Web的机器学习动态调整现有的优化策略，以及2）明智地将新的未使用的未使用的设备资源纳入页面负载中。第三，它将创建简化改变内容改编的方法，例如网络感知重播调试，以全面评估跨潜在执行环境的页面修改。这项研究将从根本上转换和改善多个玩家的Web。发达区域中的用户将经历较低的延迟或增加功能（增加网站收入），而开发区域的用户将适当访问当前无法使用其可用资源的关键应用程序。敬畏还将降低网站的开发成本，并通过概括优化来弥合网络研究和实际部署之间的差距。这项研究将由发展区域的测试床和通过与工业合作者的合作伙伴关系来告知和评估。该项目还将涉及1）一种新的教学“完整堆栈”方法，用于教授网络系统设计，2）通过网络的高度可访问的镜头来吸引代表性不足的本科生和K-12学生。作为该项目的一部分设计的研究文物和课程材料将在公共网站上发布：https：//web.cs.ucla.edu/~ravi/Awe/Awe/。此外，该项目站点将包括页面结构的汇总摘要和资源可用性（以隐私的仿真痕迹的形式），该示意图是在开发区域测试台上收集的。该网站将定期维护，项目数据将在出版后至少保存5年，并以公共利益为基础。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子和更广泛的影响评估标准来评估通过评估来诚实地支持。

项目成果

Alohamora: Reviving HTTP/2 Push and Preload by Adapting Policies On the Fly

• 发表时间: 2021
• 作者: Nikhil Kansal;M. Ramanujam;R. Netravali

Continuous prefetch for interactive data applications

• DOI: 10.14778/3407790.3407826
• 发表时间: 2020-05
• 期刊: Proceedings of the VLDB Endowment
• 影响因子: 2.5
• 作者: Haneen Mohammed

WebMedic: Disentangling the Memory-Functionality Tension for the Next Billion Mobile Web Users

• DOI: 10.1145/3446382.3448652
• 发表时间: 2021-02
• 期刊: Proceedings of the 22nd International Workshop on Mobile Computing Systems and Applications
• 作者: Usama Naseer;Theophilus A. Benson;R. Netravali

Mind the delay: the adverse effects of delay-based TCP on HTTP

• DOI: 10.1145/3386367.3431299
• 发表时间: 2020-11
• 期刊: Proceedings of the 16th International Conference on emerging Networking EXperiments and Technologies
• 作者: Neil Agarwal;Matteo Varvello;Andrius Aucinas;F. Bustamante;R. Netravali

Semeru: A Memory-Disaggregated Managed Runtime

• 发表时间: 2020
• 作者: Chenxi Wang;Haoran Ma;Siyi Liu;Yuanqi Li;Zhenyuan Ruan;Khanh Nguyen;Michael D. Bond;R. Netravali-R.