CSR: Small: Collaborative Research:Exploiting Predictability & Interdependency of Physical Parameters for Resource-Efficient Integration of Real-Time Embedded Systems

嵌入式系统越来越多地支持日常活动。在这样的系统中，违反最后期限可能导致危险和/或灾难性的后果。 这种系统的定时参数由这些系统的物理状态（例如，汽车系统中的点火正时由发动机速度确定）。 当前处理物理环境状态和定时参数之间的依赖关系的方法做出非常简单的假设，导致浪费硬件和能量资源。这项研究促进了嵌入式系统的更有效的集成，从而使更小的技术，通过结合技术，从机械工程，控制系统和实时调度。由此产生的技术将最大限度地减少无处不在并运行我们日常生活的系统的碳足迹。该项目的教育目标是将所产生的工件纳入本科和研究生课程，以及通过夏令营和挑战赛在当地学校的K-12推广。该研究旨在通过分析，建模和预测物理状态和时序参数之间的依赖关系来最大限度地减少实时嵌入式系统的计算使用，以减少尺寸，重量，以及下一代嵌入式系统的功耗要求。 一个中心目标是开发的运行时调度算法和相关的可扩展性分析的新的实时任务模型，表征系统的多个物理尺寸和它们的相互依赖性。运行时自适应协议有效地分配资源时的物理参数的估计是不准确的。 在嵌入式平台上的调度和分配算法的实现提供了多维技术的性能评估相比，传统的单维模型。

Everyday activities are increasingly enabled by embedded systems. In such systems, a deadline violation can lead to dangerous and/or disastrous consequences. The timing parameters of such systems are driven by the physical state of these systems (e.g., ignition timing in automotive systems is determined by engine speed). Current approaches to deal with the dependencies between the physical environment state and timing parameters make very simplistic assumptions, resulting in wasted hardware and energy resources. This research facilitates more effective integration of embedded systems --thus enabling smaller technology via incorporation of techniques from mechanical engineering, control systems, and real-time scheduling. The resultant techniques will minimize the carbon footprint of systems that are ubiquitous and run our everyday lives. The educational objective of this project incorporates resulting artifacts into undergraduate- and graduate-level courses, as well as in K-12 outreach at local schools via summer camps and challenge sessions.This research aims to minimize the computational utilization of real-time embedded systems by analyzing, modeling, and predicting dependencies between the physical state and timing parameters to reduce the size, weight, and power requirements of next-generation embedded systems. A central objective is development of run-time scheduling algorithms and associated schedulability analysis for new real-time task models that characterize multiple physical dimensions of the system and their interdependencies. Run-time adaptation protocols effectively allocate resources when the estimates of the physical parameters are inaccurate. Implementations of the scheduling and allocation algorithms upon embedded platforms provide evaluation of performance of multi-dimensional techniques compared to the traditional single-dimensional models.