Career: A Prediction-based Approach to Responsive Distributed Interactive Applications

在接下来的几年里，大型高分辨率显示器和沉浸式显示器将使新型交互式应用程序得以普及，这将推动对计算和通信资源的需求大幅增加。这些需求将通过使用越来越广泛的分布式计算环境（如计算网格）来满足。不幸的是，这些分布式交互应用程序的非周期性软实时需求与它们将运行的共享、无保留、高度动态和竞争的环境之间存在不匹配。为了实现响应性，这些应用程序必须调整其行为以适应其执行环境的变幻莫测。已经开发或提出了许多自适应机制，但如何控制这些机制以实现软实时约束的工作相对较少。该项目的目标是开发一种控制系统，该系统能够就如何利用其适应机制来实现这些限制向应用程序提供有效的建议。该方法是应用严格的统计预测技术来预测应用程序的资源需求如何随时间变化，以及环境的资源可用性如何随时间变化。这些预测是在运行时按需计算的，然后应用程序或其他用户级中间件服务可以使用这些预测来为应用程序的任务选择适当的映射，从而高概率地满足它们的约束。该项目的贡献将包括分布式交互应用程序中对CPU和网络资源需求的动态行为和可预测性的统计特征，分布式计算环境中这些资源可用性的动态行为和可预测性的统计特征。用于预测在线资源需求和可用性的实用工具，并为分布式交互式应用程序提供应用程序级性能预测和适应性建议，以及向研究生和本科生介绍计算机系统研究和实践背景下的统计预测技术和数据分析的课程。该项目还将产生至少两篇博士论文。该项目将建立在研究人员早期工作的基础上，该工作表明，对于一个简化的问题，即使用主机负载的时间序列预测来调度计算绑定的实时任务，本提案中描述的方法可以工作，并且可以导致上述各种贡献。除了产生科学成果、软件工件和教育学生之外，研究人员相信这里展示的路径将使他成为高性能分布式计算社区中分布式交互式应用程序基于预测的服务的权威。

Over the next few years, large high resolution displays and immersive displays will enable and popularize new kinds of interactive applications that will drive a vast increase in the demand for computational and communications resources. These demands will be met by using increasingly wider area distributed computing environments such as computational grids. Unfortunately, there is a mismatch between the aperiodic soft real-time requirements of these distributed interactive applications and the shared, unreserved, highly dynamic and competitive environments they will run in. To achieve responsiveness these applications will have to adapt their behavior to the vagaries of their execution environments. A number of adaptation mechanisms have been developed or proposed, but there is comparatively little work on how to control these mechanisms to achieve soft real-time constraints. The goal of this project is to develop a control system that can competently advise an application as to how to make use of its adaptation mechanisms to achieve such constraints. The approach is to apply rigorous statistical prediction techniques to predict both how the application's resource demands will vary over time and how the environment's resource availability will vary over time. Such predictions, which are computed on demand at run-time, can then be used by the application or other user-level middleware services to choose an appropriate mapping for the application's tasks with which they can meet their constraints with high probability. The contributions of this project will include a statistical characterization of the dynamic behavior and predictability of the demand for CPU and network resources in distributed interactive applications, a statistical characterization of the dyunamic behavior and predictability of the availability of these resources in distributed computing environments, practical tools for predicting resource demand and availability online and providing application-level performance predictions and adaptation advice to distributed interactive applications, and courses that will introduce graduate students and undergraduates to statistical prediction techniques and data analysis within the context of computer systems research and practice. The project will also produce at least two Ph.D. dissertations. This project will build on the researcher's earlier work, which has shown , for a simplified problem, namely scheduling compute-bound real-time tasks using time series predictions of host load, that the approach described in this proposal can work and can lead to the kinds of contributions described above. In addition to producing scientific results, software artifacts, and educating students, the researcher believes that the path shown here will develop his reputation as the authority on prediction-based services for distributed interactive applications within the high performance distributed computing community.