基于队列的动态电源管理策略

动态电源管理技术是一项系统级电源优化策略,它通过有选择性的关闭(或者降低性能)空闲的可控设备来降低功耗.在回顾以往系统级动态电源管理策略的基础上,提出了一种基于队列的新型系统级动态电源管理策略,利用队列不但可以提高对于可控模块何时被唤醒时间的预测命中率,并且能够根据不断变化的任务负载情况进行自身的快速调节,以适应不同任务的要求,通过搭建的SkyEye环境进行仿真,结果验证了在性能和节能方面,该方法优于传统方法.     

动态电源管理的随机切换模型与策略优化

提出一种基于连续时间Markov决策过程的动态电源管理策略优化方法.通过建立动态电源管理系统的随机切换模型,将动态电源管理问题转化为带约束的策略优化问题,并给出一种基于矢量合成的策略梯度优化算法.随机切换模型对动态电源管理系统的描述精确,策略优化算法简便有效,既能离线计算,也适用于在线优化.仿真实验验证了该方法的有效性.     

高级动态电源管理在系统软件层的设计与实现

现在可移动嵌入式系统在飞速发展,而相应的电池技术发展速度却与之不符,这就使得电源管理成为了一个迫切需要解决的问题.动态电源管理(DPM)就是一种有效的系统级电源管理策略,它在恰当的时机选择性地切换设备状态,其目的是节省系统功耗以及系统响应延时.介绍了现有的几种DPM策略,如贪婪策略、超时策略、预判策略以及随机模型最优化策略等等,通过分析现有策略的优缺点,提出了一种系统级高级动态电源管理策略(ADPM),并在软件模拟环境中实现了ADPM策略和现有的其它几种DPM策略,并通过实验将几种策略进行比较,结果表明,ADPM在系统响应速度上要平均提高13%,在功耗节省上平均要提高16%,证明ADPM是一种有效的动态电源管理策略.     

动态电源管理的随机切换模型与在线优化

考虑系统参数未知情况下的动态电源管理问题，提出一种基于强化学习的在线策略优化算法. 通过建立事件驱动的随机切换分析模型，将动态电源管理问题转化为带约束的Markov 决策过程的策略优化问题. 利用此模型的动态结构特性，结合在线学习估计梯度与随机逼近改进策略，提出动态电源管理策略的在线优化算法．随机切换模型对电源管理系统的动态特性描述精确，在线优化算法自适应性强，运算量小，精度高，具有较高的实际应用价值．     

操作系统电源管理研究进展

系统级动态电源管理技术的关键问题是在系统性能与能耗之间进行权衡.在硬件提供支持的基础上,操作系统电源管理的优势在于操作系统作为系统资源管理者,根据工作负载和硬件资源电源状态的特征,在满足系统性能约束条件下作出电源管理决策和控制,降低系统能耗.从优化控制策略和操作系统资源管理的角度,综述操作系统电源管理研究现状及其关键技术.对两类动态电源管理策略进行详细分析,讨论了从资源管理角度进行操作系统电源管理的研究思路和问题,对当前面临的主要问题和发展趋势进行了总结和展望.     

面向原油库存管理过程的Petri网建模

石油化工工业属于典型的连续型生产工业,但在连续生产过程中又包含有离散变量, 具有明显的混杂动态系统特征.对于这样的混杂动态系统,混合Petri网是一种有效的建模方法.文章在已有的混合Petri网结构基础上,在离散变迁激发规则中加入了逻辑控制条件,并对原油库存管理过程这一兼有连续时间特性和离散事件特性的混合动态系统进行建模,并通过建模实例验证了其有效性和灵活性,从而为解决原油库存问题提供了具有参考价值的方法.     

基于神经网络近似的自适应优化控制

为了解决初始和终端确定的一类离散时间非线性系统有限时间优化控制,利用动态规划原理求解过程中遇到维数灾的问题,提出了基于神经网络的自适应动态规划近似优化控制.在分析动态规划求解遇到维数灾的基础上,进而给出了迭代ADP算法,并采用神经网络近似代价函数和控制律来实现迭代ADP算法,设计近似优化控制器.通过mat lab实验仿真结果表明,采用迭代ADP算法能够避免求解中遇到的维数灾,从而有效地实现了一类离散时间非线性系统的有限时间近似优化控制.     

单处理器多外设实时系统全局能耗优化算法研究

在电池供电的实时嵌入式系统中,能耗是系统设计的一个重要研究问题.动态电压调度和动态电源管理是两种重要的节能技术.前者是动态改变处理器电压/频率,降低处理器能耗;而后者是动态调整片外设备的工作模式,减少片外设备能耗.目前只有少量研究把这两种技术综合在一起.本文研究支持这两种技术的嵌入式全系统实时任务节能调度问题.针对连续和离散处理器频率模型,论文分别提出高效的算法,通过计算系统运行的能耗最小处理器最优频率和设备最优空闲时间,来实现全系统综合节能的目的.实验模拟表明本文算法大大优于其他算法.     

基于迭代神经动态规划的数据驱动非线性近似最优调节

利用数据驱动控制思想,建立一种设计离散时间非线性系统近似最优调节器的迭代神经动态规划方法.提出针对离散时间一般非线性系统的迭代自适应动态规划算法并且证明其收敛性与最优性.通过构建三种神经网络,给出全局二次启发式动态规划技术及其详细的实现过程,其中执行网络是在神经动态规划的框架下进行训练.这种新颖的结构可以近似代价函数及其导函数,同时在不依赖系统动态的情况下自适应地学习近似最优控制律.值得注意的是,这在降低对于控制矩阵或者其神经网络表示的要求方面,明显地改进了迭代自适应动态规划算法的现有结果,能够促进复杂非线性系统基于数据的优化与控制设计的发展.通过两个仿真实验,验证本文提出的数据驱动最优调节方法的有效性.     

基于LabWindows/CVI的振动测试数据分析处理系统设计

电子设备在现代武器装备中起着至关重要的作用,其可靠性对武器装备发挥其作战能力有很大的影响.基于LabWindows/CVI软件开发平台,设计了振动测试数据分析处理系统.通过滤波、快速傅里叶变换(FFT,fast Fourier transform)等数据处理功能,对振动测试数据信号进行时域和频域分析,并且计算出电子设备振动响应传递函数,从而分析评估电子设备在装备工作过程中的振动冲击响应传递特性及影响效应,为电子设备的优化布局及装备的试验定型提供科学的试验数据参考.调试试验表明,该数据分析处理系统基本上实现了设计目的,满足了预期的设计要求及功能需求.     

System level fixed priority energy management algorithm for embedded real time application

Dynamic power management (DPM) and dynamic voltage scaling (DVS) are crucial techniques to reduce the energy consumption in embedded real-time systems. Many previous studies have focused on the energy consumption of the processor or I/O devices. In this paper, we focus on the problem of energy management integrating DVS and DPM techniques for periodic embedded real-time applications with rate monotonic (RM) policy and present a system level fixed priority energy-efficient scheduling (SLFPEES) algorithm. The SLFPEES algorithm consists of I/O device scheduling and job scheduling. I/O device scheduling is based on the dynamic power management with rate monotonic (DPM-RM) policy which puts devices into the sleep state when the idle interval is larger than devices break even time. Job scheduling is based on the RM policy and uses stack resource protocol (SRP) to guarantee exclusive access to the shared resources. For energy efficiency, the SLFPEES algorithm schedules the task with a lower speed and a higher speed. The experimental result shows that the SLFPEES algorithm can yield significantly energy savings with respect to the existing techniques.     

嵌入式系统的动态功耗管理结构

近几年来功耗问题成为在嵌入式系统领域中普遍关注的热点问题。其中动态功耗管理便是一种重要的减少系统范围的能量的方式。在近年的处理机设计技术中引入了支持基于动态电压与频率缩放的功耗管理的系统,主要提出一种基于这种技术的动态功耗管理的层次体系结构,它是基于"策略"的一种抽象定义。最后文章还用一个实例说明该结构不但能很好地完成复杂系统的功耗管理功能,而且具有较好的可扩展性,并且可以大幅度地降低系统功耗。     

Comparing system level power management policies

Reducing power consumption is a challenge to system designers. Portable systems, such as laptop computers and personal digital assistants (PDAs), draw power from batteries, so reducing power consumption extends their operating times. For desktop computers or servers, high power consumption raises temperature and deteriorates performance and reliability. Soaring energy prices and rising concern about the environmental impact of electronics systems further highlight the importance of low power consumption. Power reduction techniques can be classified as static and dynamic. Static techniques, such as synthesis and compilation for low power, are applied at design time. In contrast, dynamic techniques use runtime behavior to reduce power when systems are serving light workloads or are idle. These techniques are known as dynamic power management (DPM). DPM can be achieved in different ways; for example, dynamic voltage scaling (DVS) changes supply voltage at runtime as a method of power management. Here, we use DPM specifically for shutting down unused I/O devices. We built an experimental environment on a laptop computer running Microsoft Windows. We implemented existing power management policies and quantitatively compared their effects on power saving and performance degradation     

A DVS-assisted hard real-time I/O device scheduling algorithm

The I/O subsystem has become a major source of energy consumption in a hard real-time monitoring and control system. To reduce its energy consumption without missing deadlines, a dynamic power management (DPM) policy must carefully consider the power parameters of a device, such as its break-even time and wake-up latency, when switching off idle devices. This problem becomes extremely complicated when dynamic voltage scaling (DVS) is applied to change the execution time of a task. In this paper, we present COLORS, a composite low-power scheduling framework that includes DVS in a DPM policy to maximize the energy reduction on the I/O subsystem. COLORS dynamically predicts the earliest-access time of a device and switches off idle devices. It makes use of both static and dynamic slack time to extend the execution time of a task by DVS, in order to create additional switch-off opportunities. Task workloads, processor profiles, and device characteristics all impact the performance of a low-power real-time algorithm. We also identify a key metric that primarily determines its performance. The experimental results show that, compared with previous work, COLORS achieves additional energy reduction up to 20%, due to the efficient utilization of slack time.

非平稳自相似业务下自适应动态功耗管理

动态功耗管理(dynamic power management,简称DPM)是一种优化计算机设备能量消耗的设计技术,优化效果依赖于采用的功耗管理策略和控制算法.研究表明,传统排队论的指数分布假设不适用于DPM策略研究,DPM最优策略是超时策略,超时策略可以获得很好的节能效果的理论原因是计算机系统业务请求具有自相似性.提出了当空闲时间长度服从Pareto分布时,基于截尾均值法小样本情况下Pareto分布形状参数的稳健有效估计算法和基于窗口大小自适应技术非平稳业务请求下的DPM控制算法.实验结果表明,该算法具有很好的稳定性,在不考虑其他条件约束时,竞争率可降到1.24,在延迟率小于0.10的条件下,竞争率可降到1.47,而且算法计算负荷小.     

基于随机决策模型的动态功耗管理策略研究

由于功耗的严格约束,现代嵌入式计算终端必须采用科学的动态功耗管理策略.文中在对计算机系统的动态功耗管理(Dynamic Power Management,DPM)模型深入研究的基础上,采用改进的DPM随机决策模型,从理论上证明了DPM最优策略是确定性马尔可夫策略,这为简化DPM控制算法提供了理论依据.在实例研究中,比较了空闲时间长度服从负指数分布与Pareto分布两种情况,发现经典的空闲时间长度服从负指数分布的假设与实际情况偏差很大.Pareto分布很好解释DPM超时策略在实际应用中可以取得优良节能效果这一现象.     

Dynamic power management in wireless sensor networks

We propose an OS-directed power management technique to improve the energy efficiency of sensor nodes. Dynamic power management (DPM) is an effective tool in reducing system power consumption without significantly degrading performance. The basic idea is to shut down devices when not needed and wake them up when necessary. DPM, in general, is not a trivial problem. If the energy and performance overheads in sleep-state transition were negligible, then a simple greedy algorithm that makes the system enter the deepest sleep state when idling would be perfect. However, in reality, sleep-state transitioning has the overhead of storing processor state and turning off power. Waking up also takes a finite amount of time. Therefore, implementing the correct policy for sleep-state transitioning is critical for DPM success. It is argued that power-aware methodology uses an embedded microoperating system to reduce node energy consumption by exploiting both sleep state and active power management     

System-level integrated power management for handheld systems

We propose a system-level integrated power management scheme for battery-operated handheld systems such as cell phones and PDAs. Rather than dealing separately with each system component, we consider the interactions between CPU, WNIC (wireless network interface card), LCD, and applications, to reduce energy consumption at the system-level. Depending on the type of applications, the proposed scheme takes the interaction between CPU voltage and frequency and either LCD clock frequency or WNIC power modes, selectively, or both of them. The proposed method selects voltage for CPU in the context of LCD clock speed to reduce the system energy consumption. The application type and the power mode of WNIC are also considered to control the CPU voltage and frequency. Experimental results show that our scheme reduces the system energy consumption by as much as 30% compared to the systems of simply combining DVS (dynamic voltage scaling) and DPM (dynamic power management) or those of using no energy saving policy.