物理融合系统：负荷建模与设计优化 - 图文

Cyber physical Systems Workload Modeling and

Design Optimization 物理融合系统：负荷建模与设计优化

学号：200909030227

姓名：张聪

Paul Bogdan and Radu Marculescu Carnegie Mellon University

Built to interact with the physical world, a cyber physical system (CPS) must be efficient, reliable, and safe. To optimize such systems, a science of CPS design considering workload characteristics (e.g., self-similarity and nonstationarity) must be established. CPS modeling and design are greatly improved when statistical physics approaches—such as master equations, renormalization group theory, and fractional derivatives—are implemented in the optimization loop

物理融合系统（CPS）是为了实现与实体世界相互作用而建立的，因此该系统必须有效、可靠并且安全。为了优化这样的系统，考虑到工作负荷的特点（例如自相似性和非恒定性），物理融合系统的设计技术必须是确定的。而统计物理学方法—例如主方程，重整化群理论，还有分数阶微分—在最优化循环中的应用，使得CPS的建模和设计得到了巨大的改良。

WE LIVE IN a world in which computation, communication, and control are continuously and increasingly interwoven to produce functionally rich and energy-efficient cyber physical systems. We understand a cyber physical system (CPS) to mean a network of embedded computational devices and an associated set of wired or wireless networks that can monitor and control various physical processes that occur in the environment (e.g., a power grid, transportation and communication network, or network of medical devices). Although the focus of the embedded systems community is on building computational models for specific embedded applications, in the CPS area the goal is not only to establish a reliable communication infrastructure between

such computational elements, but also to include time- and feedback-based control as intrinsic components of the programming model.

This

goal

lets

us

generalize

the

embedded-systems

computational paradigm so that more-direct interaction between the system and physical world becomes possible. For instance, vehicular networks describing the cars’movement in a city or the swarms of bacteria used for diagnostic or drug delivery purposes are CPS examples that are distinct from classical networked embedded systems.

我们生活在一个这样的的世界，计算、通信和控制正不断地混杂在一起创造出功能强大并高能效的物理融合系统。我们推断物理融合系统（CPS）意味着一种嵌入式计算设备网络和一套联合的有线或无线的网络，这种网络可以监控和管理多种存在于某种环境下（例如一个电力网，交通和通信网络，或者医疗设备的网络）的物理进程。尽管嵌入式系统的内容中心在于为特定的嵌入式应用程式建造计算模型，但在CPS领域，这个目标就不仅仅是在这种计算单元之间建立可靠的通信基础结构，并且还包括使基于时间和反馈的操纵装置变成编制程序的固有元件。这个目标让我们总结出嵌入式系统的计算范式以便让更多计算系统与物质世界之间的直接互动变得可能。例如，用车载自组织网络描述汽车在城市中的运行轨迹或是一大群用于诊断和药物释放目的的细菌的运行轨迹就是CPS区别于传统嵌入式网络系统的例子。

A CPS must meet requirements for performance and low-power operation, as well as be safe, reliable ,and secure. Clearly, such complex requirements call not only for a new science of networks, but also for a multidisciplinary approach toward CPS design that brings together concepts and techniques from real-time computing and signal processing, as well as distributed, self-organizing control of heterogeneous sensor networks and embedded systems. Indeed, such a new science cannot rely on classical approaches for workload modeling and linear control paradigms.

物理融合系统必须满足高性能和低功耗的条件，并且要具有安全、可靠、保密的特性。当然，既然CPS的设计要满足这么复杂的条件，那么它需要的就不仅是一门新兴的网络科学，还需要一种融合了多学科的方法，这种方法包含着实时计算、信号处理、异构传感器网络的分散式自组织控制和嵌入式系统中的观念和技术。事实上，这门新学科不能依赖于传统的负荷建模和线性控制范式的方法。

A CPS workload is the amount of measured and/or processed data per unit of time that is communicated between various CPS nodes and which affects not only various local parameters (e.g., buffer utilization) but also macroscopic metrics (e.g., CPS throughput). For instance, we cannot decide the size and topology of a particular wireless sensor network without considering the spatiotemporal characteristics of the communication workload that must be communicated reliably to data centers for further analysis and decision purposes. What’s more, we cannot arbitrarily decide the size of the communication buffers between the sensors in a network or data center because the loss or delay of critical information can have catastrophic effects on air, road, or railroad traffic. Similarly, we cannot ignore the characteristics of the workload generated by a series of bio-implantable devices, because this can have a crucial impact on a patient’s life.

物理融合系统的工作负载指的是每单位在各个CPS节点间的通讯时间内测量和被处理的数据总量，它不仅影响到各种局部参数（例如缓冲使用情况），还影响到宏观测度（例如CPS吞吐量）。举一个例子，我们没有办法在不考虑通讯负载时空相关特性的情况下决定一个特定无线传感网络的容量和拓扑结构，因为为了进一步的解析和决策，通讯负载必须要可靠的传递给数据交换中心。此外，我们不能武断的决定网络或信息交换中心的传感器之间的通信缓冲器的容量，因为关键信息的损耗或延迟可能会在航空、公路或铁路交通中带来灾难性后果。同样的，我们也不能忽视一系列通过可植入式生命设备总结出的工作负荷的特性，因为忽视它们可能会给病人的生命带来决定性的影响。

From

this

perspective,

we

argue

that

precise

workload

characterization should be one of the main drivers in CPS design and optimization. Consequently, in this article we propose a new framework for workload characterization based on statistical physics and then discuss how this new vision can improve the design of future cyber physical systems.

从这个角度来看，我们认为精确的工作负荷特性表示法应该作为CPS设计和优化的主要驱动程序之一。因此，在这篇文章中我们会提出一种基于统计物理学的工作负荷特性表示法的新型框架，然后讨论这个新版本是如何来改善未来的物理融合系统设计的。

Figure 1. R-R intervals, collected via electrocardiogram recording of data from a healthy subject, exhibit self-similarity (a). Mean, variance, and kurtosis plots of the R-R intervals (as a function of the beat number) for a healthy individual exhibit nonstationary behavior, which deviates from Gaussian statistics (b). Power spectral density of the R-R intervals exhibits a 1/f(β=1.445) behavior confirming the self-similarity assumption (c).(The R-R interval data sets were obtained from the National Institute of Biomedical Imaging and Bioengineering website; http://www.physionet. org.)

Main characteristics of physical processes 物理过程的主要特性

All the CPS components that measure physical parameters—temperature, humidity, speed, heart rate, and so on—can be described by a set of concurrent processes that interact and adapt to changing environmental conditions. Such physical processes typically induce a dynamic

system

behavior

in

response

to

various

external

β

(environmental) stimuli. For instance, the earth’s weather, or a crowd’s behavior (which is directly relevant to vehicular traffic modeling, rarely operate at equilibrium. Even when the earth’s weather or a crowd’s behavior reaches a steady state, this occurrence typically happens only for short intervals of time.

所有测量诸如温度、湿度、速度、心率等等物理参数的CPS组件可以被描述为一组与

不断变化的环境条件相互影响并最终适应的并发进程。这些物理过程通常包括对各种外部（环境）刺激做出反应的动力系统行为。例如，地球的天气，或者某种与交通运输工程建模直接相关的群体行为，就很少在平衡状态下运作。即使当地球的天气或这种群体行为达到稳定状态，这种情况也通常仅仅发生在一段很短的时间间隔内。

Despite their complex behavior, physical processes can be characterized by self-similarity and nonstationarity. For instance, in Figure 1a we plot the R-R intervals—that is, the time duration between two R waves in an electrocardiogram (ECG) signal—as a function of the number of heartbeats for a healthy individual. An ECG signal represents the electrical activity of the heart over time and consists of four elements: P wave (atrial depolarization), QRS complex (ventricles’depolarization),

T

wave

(ventricles

’depolarization),

and

U

wave(interventricular septum repolarization).Because the ventricles contain more muscles than atria, the spike corresponding to an R wave (heartbeat) in the QRS complex appears more visible than other waves. The difference between two consecutive R waves is called the R-R interval and provides information about heart rate variability. By zooming in across several time scales with respect to the initial time series, we can see that the spiky dynamics observed in different intervals and subintervals display some sort of similar statistical irregularity; this represents a self-similar behavior (Figure 1c).

除了这些复杂的特性，物理过程还可能具有自相似性和非平稳性的特征。举个例子，在图1a中我们标绘出R-R的区间，即是心电图信号中两个R波形之间的持续时间，作为一个健康个体心率数目的函数。一幅心电图信号描绘出心脏随着时间的电活动和组成他的四种元素：P波形（心房除极化）、QRS复合波群（心室除极化）、T波形（心室再极化）、和U波形（心室间隔膜复极化）。因为心室比心房包含更多的肌肉，所以QRS复合波群中对应于R波形（心率）的峰电位比其他波形明显得多。两个连续R波形之间的不同被叫做R间期，它提供了心率变异率的信息。通过放大关于初始时间序列的一些时间尺度，我们可以看到不同的间距都遵守着一些尖锐的动态图案，而且子间距都表现出某类相似的经统计的无规律现象；这些都表现出它自相似特征。

The spiky behavior evidenced in Figure 1 has important implications

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