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

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、Web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，以满足用户，这并不总是通过在反应之前等待用户的决定来满足。 另一种方法是根据用户的选择预取数据。 预取的现有使用存在几个限制：（1）它通常以针对每个应用程序的特定方式开发，并且不考虑所有优化方面，以及（2）它们没有明确地利用许多交互式应用程序的近似容忍性质。 近似容忍度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。本项目设计了一个通用预取框架，称为GPF，它显式地从客户端应用程序中删除预测和调度。 可配置的预测模型估计在不同的未来时间间隔请求的可能性，并且通用调度器使用这些预测来决定向客户端发送哪些请求。 这个框架在几个方面是新颖的：（1）客户端偶尔向调度器提供预测，而不是显式请求，调度器在将结果推送到客户端时考虑网络和资源条件，（2）GPF利用应用容限来发送大量候选请求的部分结果，而不是少数请求的全部结果，以及（3）GPF基于延迟、网络和资源条件动态地改变预测器和调度器计算在客户端或服务器上的位置。支持研究将来自网络社区的性能和调度思想与优化、存储以及来自数据库和可视化社区的交互想法。 GPF将在多个领域的应用程序中集成并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，并将对交互式应用程序的开发方式产生重大的长期影响。 研究和教育材料的成果将通过讲习班、出版物和开放源储存库传播。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，并最终使越来越多的依赖或需求，该奖项反映了NSF的法定使命，并被认为值得支持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。

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.