操作系统级低功耗动态电压缩放算法分析

低功耗的设计已经成为嵌入式系统设计中一个非常重要的方面，而动态电压调度（Dynamic Voltage Scaling DVS）又被认为是降低功耗的一种有效手段。本文对各类针对系统的动态电压缩放算法做了较系统的总结，给出了算法的模型，重点描述了操作系统级的两类动态电压缩放算法——基于间隔和基于任务的动态电压调度算法，概述了针对编译级的任务内动态电压调度算法。文章对三类算法作了分析与比较，由此给出了结论与观点，对以后动态电压缩放算法的研究做了预测。     

基于功耗大小的DVS频率选择策略

动态电压调节技术（DynamicVoltageScaling,DVS）是一种有效的运用于实时嵌入式系统中的低功耗技术。动态电源管理（DynamicPowerManagement,DPM）是一种通过选择性关闭处于欠负载状态的模块,使系统功耗最小化的策略。实时嵌入式系统中DVS技术不仅要实现系统功耗的降低,同时也要兼顾系统的实时性。但是,单纯的DVS技术或是DPM技术都不能完全解决实时嵌入式系统中的功耗问题。文章针对已有的动态电压调节策略,分析DPM策略在动态电压调节过程中对系统总功耗的影响,从而提出基于功耗大小的DVS控制策略。     

基于非线性因子的改进鸟群算法在动态能耗管理中的应用

针对实时系统能耗管理中动态电压调节(DVS)技术的应用会导致系统可靠性下降的问题,该文提出一种基于改进鸟群(IoBSA)算法的动态能耗管理法。首先,采用佳点集原理均匀地初始化种群,从而提高初始解的质量,有效增强种群多样性;其次,为了更好地平衡BSA算法的全局和局部搜索能力,提出非线性动态调整因子;接着,针对嵌入式实时系统中处理器频率可以动态调整的特点,建立具有时间和可靠性约束的功耗模型;最后,在保证实时性和稳定性的前提下,利用提出的IoBSA算法,寻求最小能耗的解决方案。通过实验结果表明,与传统BSA等常见算法相比,改进鸟群算法在求解最小能耗上有着很强的优势及较快的处理速度。     

机载合成孔径雷达实时数字成象处理器的整机控制系统

本文对机载合成孔径雷达实时数字成象处理器整机控制系统的系统组成及技术特点进行了研究，扼要阐述了运行在整机控制系统中的实时操作系统的通讯、任务调度等机制。本文讨论了在产时操作系统中整机控制系统多任务的调度、通讯及多处理器之间的通讯、数据传递等问题，以及实时系统中“人机对话”问题，提出了解决问题的方法和系统的控制流程。     

嵌入式RTOS中任务优先级反转问题研究

针对嵌入式实时系统任务调度策略的特点，任务在运行的时候可能被更高优先级的任务中断。分析了嵌入式RTOS中的最高就绪任务的查找算法，指出该调度算法存在的不同优先级的反转问题，并且针对这一问题进行研究，给出了相应的优先级继承方案。较好地解决了RTOS中任务调度中的优先级的反转问题。     

几种实时嵌入式系统DVS策略的分类比较

动态电压调节是一种有效的运用于实时嵌入式系统中的低功耗技术。实时嵌入式系统DVS技术不仅要实现系统功耗的降低,同时也要兼顾系统的实时性,满足任务的截止时间限。该文针对近几年实时嵌入式系统中DVS策略,首先介绍实时系统中DVS策略模型,对主流策略进行分类比较,并且对相应策略进行仿真,DVS策略可以取得10%～40%的能耗节省。     

嵌入式实时操作系统任务调度算法优化

在嵌入式系统中,任务调度器的好坏很大程度上决定了系统的性能.该文分析了实时系统中有代表性的静态以及动态调度算法,在此基础上,结合RMS和EDF算法各自的优点,对嵌入式实时内核μc/os-Ⅱ的调度算法进行了优化.     

机载合成孔径雷达实时数字成象处理器的整机控制系统

本文对机载合成孔径雷达实时数字成象处理器整机控制系统的系统组成及技术特点进行了研究,扼要阐述了运行在整机控制系统中的实吋操作系统的通讯、任务调度等机制。本文讨论了在实时操作系统中整机控制系统多任务的调度、通讯及多处理器之间的通讯、数据传递等问题,以及实时系统中人机对话的问题,并提出了解决问题的方法和系统的控制流程。     

一种基于Vxworks的多任务调度算法

实时操作系统的任务调度算法是研究嵌入式系统开发的热点。针对机载二次配电系统（the Second Power Discribution Association,缩写为sPDA）的任务调度．提出了一种在信号量通信的基础上选择时间片轮转的静态权重优先级抢占混合的调度算法,并对算法进行了仿算。     

RTAI实时调度算法及其调度器的改进

在实时系统中,任务调度策略是内核设计的关键部分.如何进行实时的任务调度,使任务能在特定的周期内完成是实时操作系统领域研究的一个热点问题.文中将一种基于RM算法的改进算法CPSS算法引入到RTAI调度器中,针对RTAI调度器在系统过载情况下出现调度性能下降等缺点,对RTAI调度器进行优化和改进.对改进后的调度器在调度时延方面和调度算法仿真方面进行了测试,实验证明了改进后的调度器能够提高Linux系统的实时性.     

硬实时系统中基于任务同步及节能的动态调度算法

提出基于任务同步及节能的动态实时调度算法HDSA(hybrid dynamic scheduling algorithm),以有效地解决任务同步及节能的难题.HDSA 结合RM及EDF算法,在满足任务实时可调度性及任务同步的限制条件下,采用DVFS节省能耗.HDSA包含静态算法及动态算法两部分.静态算法在静态条件下,求出任务的静态速度.动态调度算法在实际运行中,固定临界区的运行速度,并充分回收、利用任务运行时的空闲执行时间,调节处理器的速度,以有效降低能耗并满足实时可调度性.同时避免高优先权任务被阻塞时,临界区继承高优先权任务的速度时所造成的处理器电压开关的频繁切换,因而能有效地降低实时任务调度的成本.实验测试表明,HDSA在调度性能上明显优于目前所知的有效算法.     

Energy-aware wireless systems with adaptive power-fidelity tradeoffs

Wireless networked embedded systems, such as multimedia terminals, sensor nodes, etc., present a rich domain for making energy/performance/quality tradeoffs based on application needs, network conditions, etc. Energy awareness in these systems is the ability to perform tradeoffs between available battery energy and application quality requirements. In this paper, we show how operating system directed dynamic voltage scaling and dynamic power management can provide for such a capability. We propose a real-time scheduling algorithm that uses runtime feedback about application behavior to provide adaptive power-fidelity tradeoffs. We demonstrate our approach in the context of a static priority-based preemptive task scheduler. Simulation results show that the proposed algorithm results in significant energy savings compared to state-of-the-art dynamic voltage scaling schemes with minimal loss in system fidelity. We have implemented our scheduling algorithm into the eCos real-time operating system running on an Intel XScale-based variable voltage platform. Experimental results obtained using this platform confirm the effectiveness of our technique     

Elastic DVS Management in Processors With Discrete Voltage/Frequency Modes

Applying classical dynamic voltage scaling (DVS) techniques to real-time systems running on processors with discrete voltage/frequency modes causes a waste of computational resources. In fact, whenever the ideal speed level computed by the DVS algorithm is not available in the system, to guarantee the feasibility of the task set, the processor speed must be set to the nearest level greater than the optimal one, thus underutilizing the system. Whenever the task set allows a certain degree of flexibility in specifying timing constraints, rate adaptation techniques can be adopted to balance performance (which is a function of task rates) versus energy consumption (which is a function of the processor speed). In this paper, we propose a new method that combines discrete DVS management with elastic scheduling to fully exploit the available computational resources. Depending on the application requirements, the algorithm can be set to improve performance or reduce energy consumption, so enhancing the flexibility of the system. A reclaiming mechanism is also used to take advantage of early completions. To make the proposed approach usable in real-world applications, the task model is enhanced to consider some of the real CPU characteristics, such as discrete voltage/frequency levels, switching overhead, task execution times nonlinear with the frequency, and tasks with different power consumption. Implementation issues and experimental results for the proposed algorithm are also discussed     

Simultaneous Dynamic Voltage Scaling of Processors and Communication Links in Real-Time Distributed Embedded Systems

Dynamic voltage scaling has been widely acknowledged as a powerful technique for trading off power consumption and delay for processors. Recently, variable-frequency (and variable-voltage) parallel and serial links have also been proposed, which can save link power consumption by exploiting variations in the bandwidth requirement. This provides a new dimension for power optimization in a distributed embedded system connected by a voltage-scalable interconnection network. At the same time, it imposes new challenges for variable-voltage scheduling as well as flow control. First, the variable-voltage scheduling algorithm should be able to trade off the power consumption and delay jointly for both processors and links. Second, for the variable-frequency network, the scheduling algorithm should not only consider the real-time constraints, but should also be consistent with the underlying flow control techniques. In this paper, we address joint dynamic voltage scaling for variable-voltage processors and communication links in such systems. We propose a scheduling algorithm for real-time applications that captures both data flow and control flow information. It performs efficient routing of communication events through multihops, as well as efficient slack allocation among heterogeneous processors and communication links to maximize energy savings, while meeting all real-time constraints. Our experimental study shows that on an average, joint voltage scaling on processors and links can achieve 32% less power compared with voltage scaling on processors alone     

Design of a hard real-time multi-core testbed for energy measurement

This paper presents a systematic methodology for designing a hard real-time multi-core testbed to validate and benchmark various rate monotonic scheduling (RMS)-based task allocation and scheduling schemes in energy consumption. The hard real-time multi-core testbed comprises Intel Core Duo T2500 processor with dynamic voltage scaling (DVS) capability and runs the Linux Fedora 8 operating system supporting soft real-time scheduling. POSIX threads API and Linux FIFO scheduling policy are utilized to facilitate the design and Dhrystone-based tasks are generated to verify the design. A LabView-based DAQ system is designed to measure the energy consumption of CPU and system board of the testbed. A case study of task allocation and scheduling algorithms is also presented that aim to optimize the schedule feasibility and energy consumed by the processor and memory module in the multi-core platform. The experience from the implementation is summarized to serve as potential guidelines for other researchers and practitioners.     

A multiprocessor-oriented power-conscious scheduling algorithm for periodic tasks

With the rapid development of advanced technology in VLSI circuit designs, many processors could provide dynamic voltage scaling (DVS) to save power consumption when the supply voltage is allowed to be lower. In this paper, we propose a multiprocessor-oriented power-conscious scheduling algorithm for the real-time periodic tasks with task migration constrained scheme. We classify periodic tasks into fixed tasks and migration tasks, and limit the number of migration tasks and the number of destination processors which execute migration tasks. The proposed algorithm is made up of two steps. Firstly, choosing a processor to sort all of the periodic tasks in a non-increasing order according to task utilization, afterwards, allocating them to other processors. Secondly, scheduling the migration tasks with a virtual execution windows policy, and then scheduling the fixed tasks with EDF algorithm. The experiment results show that compared with arbitrary task migration policy and no task migration allowed policy, the power consumption in multiprocessor real-time periodic tasks scheduling is lowered significantly with the proposed algorithm.     

基于多核处理器的节能任务调度方法

由于关系到系统的安全性及散热代价等方面,能耗问题已经成为嵌入式系统研究的重点。对于多核处理器上具有依赖关系的周期性硬实时任务,设计了一种基于动态电压调节的节能任务调度方法。该方法首先用RDAG算法将任务独立化,然后以功耗最低为原则,采用遗传算法确定任务映射。基于Intel PXA270功耗模型,采用了几个随机任务集进行仿真实验,结果表明该方法比现有的方法节省了20%～30%的能耗。     

Chameleon: Application-Level Power Management

In this paper, we present Chameleon an application-level power management approach for reducing energy consumption in mobile processors. By using application domain knowledge, as opposed to OS-level or hardware-level inferred knowledge, Chameleon can substantially reduce CPU energy consumption. By exporting the energy management to user-space, designers can design more flexible and easily portable algorithms and systems, and use multiple energy management policies simultaneously. Specifically, we propose a minimal operating system interface that applications use to obtain global knowledge from the kernel in order to make local decisions. We consider three classes of applications soft real-time, interactive and batch and design user level power management strategies for representative applications such as a movie player, a word processor, a web browser, and a batch compiler. Our experiments show that, compared to the traditional system-wide CPU voltage scaling approaches, Chameleon can achieve up to 32-50% energy savings while delivering comparable or better performance to applications. Similarly, Chameleon extracts 9-41% more energy when compared to Grace OS, which uses some application knowledge but operates within the kernel. Further, Chameleon imposes minimal overhead and is effective at scheduling concurrent applications with diverse energy needs.     

Real-Time Dynamic Voltage Loop Scheduling for Multi-Core Embedded Systems

In this brief, we propose a novel real-time loop-scheduling technique to minimize energy consumption via dynamic voltage scaling (DVS) for applications with loops considering transition overhead. One algorithm, dynamic voltage loop scheduling (DVLS), is designed integrating with DVS. In DVLS, we repeatedly regroup a loop based on rotation scheduling and decrease the energy by DVS as much as possible within a timing constraint. We conduct the experiments on a set of digital signal processing benchmarks. The experimental results show that DVLS achieves big energy saving compared with the traditional time-performance-oriented scheduling algorithm     

Real-time multimedia processing in video sensor networks

Video sensor networks (VSNs) has become the recent research focus due to the rich information it provides to address various data-hungry applications. However, VSN implementations face stringent constraints of limited communication bandwidth, processing capability, and power supply. In-network processing has been proposed as efficient means to address these problems. The key component of in-network processing, task mapping and scheduling problem, is investigated in this paper. Although task mapping and scheduling in wired networks of processors has been extensively studied, their application to VSNs remains largely unexplored. Existing algorithms cannot be directly implemented in VSNs due to limited resource availability and shared wireless communication medium. In this work, an application-independent task mapping and scheduling solution in multi-hop VSNs is presented that provides real-time guarantees to process video feeds. The processed data is smaller in volume which further releases the burden on the end-to-end communication. Using a novel multi-hop channel model and a communication scheduling algorithm, computation tasks and associated communication events are scheduled simultaneously with a dynamic critical-path scheduling algorithm. Dynamic voltage scaling (DVS) mechanism is implemented to further optimize energy consumption. According to the simulation results, the proposed solution outperforms existing mechanisms in terms of guaranteeing application deadlines with minimum energy consumption.