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实现并普及新型交互式应用，这将推动对计算和通信资源的需求大幅增长。 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，这些分布式交互式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，这些应用程序必须使其行为适应其执行环境的变幻莫测。已经开发或提出了一些自适应机制，但是关于如何控制这些机制以实现软实时约束的工作相对较少。 这个项目的目标是开发一个控制系统，可以胜任建议的应用程序如何利用其适应机制，以实现这样的约束。 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将如何随时间变化，以及环境的资源可用性将如何随时间变化。 在运行时按需计算的这种预测然后可以由应用或其他用户级中间件服务用于为应用的任务选择适当的映射，利用该映射，它们可以以高概率满足它们的约束。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，在线预测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建议，以及在计算机系统研究和实践的背景下向研究生和本科生介绍统计预测技术和数据分析的课程。 该项目还将产生至少两个博士学位。学位论文该项目将建立在研究人员的早期工作，这表明，对于一个简化的问题，即调度计算绑定的实时任务，使用主机负载的时间序列预测，在这个建议中描述的方法可以工作，并可以导致上述的贡献。 除了产生科学成果，软件工件和教育学生，研究人员认为，这里显示的路径将发展他的声誉，在高性能分布式计算社区内的分布式交互应用程序的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.