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.

交互性是各种面向用户的应用程序的核心要求，包括数据可视化、网络搜索和游戏。 这些面向用户的交互式应用程序必须实现低延迟响应才能满足用户的需求，而通过在做出反应之前等待用户的决定并不总是能够满足用户的需求。 另一种方法是根据用户的选择预取数据。 预取的现有使用有几个限制：（1）它通常是针对每个应用程序以临时方式开发的，并且没有考虑所有优化方面，以及（2）它们没有明确利用许多应用程序的近似容忍性质交互式应用程序。 近似容差意味着用户更喜欢快速但近似的结果，而不是完全正确但缓慢的结果。该项目设计了一个称为 GPF 的通用预取框架，它明确地将预测和调度与客户端应用程序解耦。 可配置的预测模型估计未来不同时间间隔的请求的可能性，通用调度程序使用这些预测来决定将哪些请求发送到客户端。 该框架在几个方面都很新颖：(1) 客户端偶尔会向调度程序提供预测，而不是显式请求，调度程序在将结果推送到客户端时会考虑网络和资源条件，(2) GPF 利用应用程序容差来发送部分结果大量候选请求，而不是少数请求的完整结果，并且 (3) GPF 根据延迟、网络和资源条件动态转移预测器和调度器计算在客户端或服务器上的位置。支持研究汇集了来自网络社区的性能和调度想法来自数据库和可视化社区的优化、存储和交互想法。 GPF 将集成并消除跨多个领域的应用程序中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）将被整合到数据科学、数据库、网络和可视化课程中。 软件将是开源的，将对交互式应用程序的开发方式产生重大、长期的影响。 研究成果和教育材料将通过研讨会、出版物和开源存储库传播。这些教育和推广计划将进一步增加对这一多学科主题的参与，这将导致大数据可视化技术、网络调度和优先级设计的持续进步，并最终使越来越多依赖或需要交互式应用程序的领域受益。做出时间紧迫的决策和发现。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

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: 2022-06-27
• 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
• 通讯作者: R. Netravali

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