Real-Time Decision Policies With Predictable Performance
Real-Time Decision Policies With Predictable Performance
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DOI：
10.1109/jproc.2018.2853608
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发表时间：
2018-09-01
影响因子：
20.6
通讯作者：
Rodionova, Alena
中科院分区：
文献类型：
--
作者：
Abbas, Houssam;Alur, Rajeev;Rodionova, Alena
随着网络物理系统(CP)的方法和工具在功能和用途上的增长，一刀切的解决方案开始显示出它们的局限性。具体地说，与通用语言和工具相比，特定于应用领域的用于编程算法或对CP建模的工具和语言通常更有用，并产生更好的性能。在心律失常监测领域，一种小型植入式医疗设备持续监测患者的心率，并在需要时提供电疗。这些设备执行的算法是流算法，所以它们最好是用流语言编写的，这样程序员就可以将传入的数据流作为基本对象进行推理，而不是强迫她考虑更低级别的细节，比如状态维护和最小化。因为这些设备是资源受限的，所以如果编程语言允许在处理运行时和能耗方面或更一般的成本方面允许可预测的性能，这是很有用的。StreamQRE是一种声明式流编程语言，具有高效、可移植的实现和强大的理论保证。特别是，它的评估算法保证了每个数据项的成本(运行时、内存、能量)不变，并计算了每个项的成本上限。这种对成本的估计允许及早探索算法的可能性，同时保持对最坏情况性能的处理，在此基础上可以设计硬件和调整算法。
As methods and tools for cyber-physical systems (CPS) grow in capabilities and use, one-size-fits-all solutions start to show their limitations. In particular, tools and languages for programming an algorithm or modeling a CPS that are specific to the application domain are typically more usable, and yield better performance, than general-purpose languages and tools. In the domain of cardiac arrhythmia monitoring, a small, implantable medical device continuously monitors the patient's cardiac rhythm and delivers electrical therapy when needed. The algorithms executed by these devices are streaming algorithms, so they are best programmed in a streaming language that allows the programmer to reason about the incoming data stream as the basic object, rather than force her to think about lower-level details like state maintenance and minimization. Because these devices are resource-constrained, it is useful if the programming language allowed predictable performance in terms of processing runtime and energy consumption, or more general costs. StreamQRE is a declarative streaming programming language, with an efficient and portable implementation and strong theoretical guarantees. In particular, its evaluation algorithm guarantees constant cost (runtime, memory, energy) per data item and also calculates upper bounds on the per-item cost. Such an estimate of the cost allows early exploration of the algorithmic possibilities, while maintaining a handle on worst case performance, on the basis of which hardware can be designed and algorithms can be tuned.