Collaborative Research: CNS Core: Medium: A Unified Prefetch Framework for Approximation-Tolerant Interactive Applications

合作研究：CNS Core：Medium：用于近似容忍交互式应用程序的统一预取框架

• 批准号: 2140552
• 负责人: Ravi Netravali
• 金额: $ 20万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2140552&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Medium

Interactivity is a core requirement for a wide range of user-facing applications, including data visualizations, web search and games. These user-facing, interactive apps must achieve low latency responses in order to satisfy users, which cannot always be met by waiting for a user's decision before reacting. An alternative is to pre-fetch data in anticipation of users' choices. There are several limitations to existing uses of prefetching: (1) it is often developed in an adhoc way for each application, and does not consider all optimization aspects, and (2) they do not explicitly take advantage of the approximation tolerant nature of many interactive applications. Approximation tolerance means that users prefer fast but approximated, over fully correct but slow, results.This project designs a General Prefetching Framework called GPF that explicitly decouples prediction and scheduling from the client application. A configurable prediction model estimates the likelihood of requests at different future time intervals, and a general scheduler uses these predictions to decide which requests to send to the client. This framework is novel in several ways: (1) rather than explicit requests, the client occasionally offers predictions to the scheduler, which considers network and resource conditions when pushing results to the client, (2) GPF exploits application tolerance to send partial results for a massive number of candidate requests, rather than full results for a few requests, and (3) GPF dynamically shifts placement of the predictor and scheduler computation on the client or server based on latency, network, and resource conditions.The supporting research brings together performance and scheduling ideas from the networking community with optimization, storage, and interaction ideas from the database and visualization communities. GPF will integrate and eliminate user-perceived application latency in applications across multiple domains, including data visualization, media players, webpage navigation, vehicular control, and games. The multidisciplinary research (networking, information visualization, and database systems) will be integrated into courses on data science, databases, networking, and visualization. Software will be open sourced, and will have significant, long-term impact on the way interactive applications are developed. The outcomes of the research and education material will be disseminated via workshops, publications, and open-source repositories. These education and outreach plans will further increase participation in this multidisciplinary topic that will lead to the continuing advancement of big data visualization techniques, network scheduling and prioritization designs, and ultimately benefit the increasing number of domains that rely on, or demand, interactive applications to make time critical decisions and discoveries.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.

交互性是针对各种面向用户的应用程序的核心要求，包括数据可视化，Web搜索和游戏。 这些面向用户的交互式应用必须达到低潜伏期响应以使用户满足用户，在反应之前，不能总是等待用户的决定来满足用户。 另一种选择是预先提取数据，以期预期用户的选择。 预摘要的现有用途有几个局限性：（1）通常为每个应用程序以Adhoc的方式开发它，并且并不考虑所有优化方面，并且（2）它们没有明确利用许多交互应用的近似值。 近似公差意味着用户更喜欢快速但近似，而不是完全正确但缓慢，结果。该项目设计了一个名为GPF的一般预取框架，该框架明确地将预测和调度与客户端应用程序分解。 可配置的预测模型在不同的未来时间间隔估算请求的可能性，并且一般调度程序使用这些预测来决定要发送到客户端的请求。 该框架以几种方式是新颖的：（1）而不是明确的请求，客户偶尔会为调度程序提供预测，在向客户推动结果时考虑了网络和资源条件，（2）GPF利用应用程序的耐受性来发送部分结果，以向大量的候选请求发送部分，而不是针对少数候选人的候选请求，而不是针对几个客户的全部调整，以及（3）GPF的安置，以及3）延迟，网络和资源条件。支持研究汇集了网络社区的性能和调度思想，并通过数据库和可视化社区的优化，存储和互动想法进行了整合。 GPF将在跨多个域的应用程序中集成和消除用户感知的应用程序延迟，包括数据可视化，媒体播放器，网页导航，车辆控制和游戏。 多学科研究（网络，信息可视化和数据库系统）将集成到有关数据科学，数据库，网络和可视化的课程中。 软件将是开源的，并将对交互式应用程序的开发方式产生重大的长期影响。 研究和教育材料的结果将通过研讨会，出版物和开源存储库来传播。这些教育和宣传计划将进一步增加参与这个多学科主题，这将导致大数据可视化技术，网络计划和优先级设计的持续发展，并最终使越来越多的领域依赖或需求的互动式应用受益，这些互动式应用程序可以通过启用NSF的构建范围来代表NSF的构建范围。影响审查标准。

项目成果

Marauder: synergized caching and prefetching for low-risk mobile app acceleration

• DOI: 10.1145/3458864.3466866
• 发表时间: 2021-06
• 期刊: Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services
• 作者: M. Ramanujam;H. Madhyastha;R. Netravali

Floo: automatic, lightweight memoization for faster mobile apps

• DOI: 10.1145/3498361.3538929
• 发表时间: 2022-06
• 期刊: Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services
• 作者: M. Ramanujam;Helen Y. Chen;Shaghayegh Mardani;R. Netravali