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。     

基于 DSPC6678多核平台的实时任务调度架构设计分析

分析了数字信号处理器（DSP） C6678的多核模式,设计了一种基于C6678高速多核DSP硬件平台的实时任务调度软件架构,实现了实时任务调度。通过实际测试,整体设计满足了设计指标。     

一种新的基于可配置处理器的异构多核线程级动态调度模型

本文针对基于可配置处理器的异构多核结构,提出一种新的线程级动态调度模型。此类异构多核系统中每个核分别针对某一应用做指令集扩展,调度器通过线程、处理器核以及指令集间的映射关系,动态调度线程至适合的处理器核,从而在没有大幅增加芯片面积的前提下,达到与每个核都具有全扩展指令集相近似的加速比,此外该模型还可以有效减少编程模型的复杂度。     

传感器网络的任务双效节能调度研究

能源供应有限性是局限传感器网络的性能和存活寿命的重要因素,本文从传感器网络节点的任务调度出发,提出动态能量管理DPM和动态电压/频率调节DV/FS的双效处理器节能调度算法,即DV/FS-RM和DV/FS-EDF调度算法;在DPM动态控制空闲任务进入休眠的同时,在保证节点的实时性的前提下,通过DV/FS-RM或DV/FS-EDF算法降低处理器频率,达到更好的节能效果.实验显示,该节能任务调度算法使以电池为能源的传感器网络节点的生存期成倍地延长.     

基于AUTOSAR4.2标准嵌入式多核操作系统的实现

介绍了AUTOSAR标准产生的背景及软件架构,阐述了多核处理器的启动、AUTOSAR多核操作系统的启动与实现过程。以Vector的软件配置工具配置符合AUTOSAR标准的嵌入式多核操作系统,在英飞凌AURIX/TC275三核单片机上运行该操作系统从而验证AUTOSAR多核操作系统的可行性。     

多核Web服务器中基于分配矩阵的动态请求调度算法

为了构建高性能的Web服务器,充分利用Web服务器中多核处理器的性能成为关键。传统的先到先服务策略没有考虑多核处理器的特点,不能充分利用多核处理器的性能。为解决此问题,该文提出一种基于分配矩阵的动态请求调度算法。该算法充分考虑了多核处理器的特点,可将同类动态请求动态分配至同一个处理器核心,提高了Web服务器处理动态请求的速度。仿真实验结果表明,采用该算法的Web服务器在自相似性、平均响应时间、丢包率等方面均优于传统的先到先服务算法。     

基于多核处理器的并行雷达数据处理研究

基于多核处理器结构,对雷达数据处理系统进行了并行处理适用性研究。通过对数据处理各个步骤进行分析,设计了并行雷达数据处理架构。实验结果表明,在4核处理器上并行数据处理系统性能比传统的串行系统提高了将近3倍,说明雷达数据处理系统是适合并行处理的。     

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。     

可变压处理器上硬实时系统的作业调度

在设计实时嵌入式系统时，如果能够善于利用可变电压处理器。可以极大减少系统的能耗。介绍了在动态优先级和静态优先级情况下，确定调度某个给定作业集所需最低电压常量，确定可变电压处理器的最优电压调度方案的思想和算法。     

基于多核DSP的实时图像处理平台研究

随着科学技术的发展,数字信号处理技术也不断地发生变化,这样一来,嵌入式系统的复杂程度变得越来越高,现今,社会中的多个领域都已经应用了嵌入式系统,而且随着这些领域的发展,对系统的实时性及可靠性要求更高,因此,嵌入式系统原有的单核处理器已经无法满足当前的要求,必须要进行优化,于是,文章对基于多核DSP的实时图像处理平台的搭建进行了研究,以便于更好地发挥嵌入式系统的功能。     

动态电压调整多处理器实时系统任务调度

动态电压调整DVS（Dynamic Voltage Scaling）是根据处理器电压（速度）降低之后，能量消耗平方级的减少这一原理提出的。文章通过DVS机制在多处理器实时系统中进行任务调度．通过对任务调度中的静态能量管理进行分析，在此基础上提出了一种新的基于DVS的适用于多处理器实时系统中的调度算法。这种新的调度算法是通过对贪婪法调度进行研究，发现其不足．并以此为基础进行改进。结合了动态电压调整的多处理器实时系统任务调度的能量消耗比普通的任务调度能量消耗有了很大的改善。     

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     

Combining Coarse-Grained Software Pipelining with DVS for Scheduling Real-Time Periodic Dependent Tasks on Multi-Core Embedded Systems

In this paper, we combine coarse-grained software pipelining with DVS (Dynamic Voltage/Frequency Scaling) for optimizing energy consumption of stream-based multimedia applications on multi-core embedded systems. By exploiting the potential of multi-core architecture and the characteristic of streaming applications, we propose a two-phase approach to solve the energy minimization problem for periodic dependent tasks on multi-core processors with discrete voltage levels. With our approach, in the first phase, we propose a coarse-grained task-level software pipelining algorithm called RDAG to transform the periodic dependent tasks into a set of independent tasks based on the retiming technique (Leiserson and Saxe, Algorithmica 6:5–35, 1991). In the second phase, we propose two DVS scheduling algorithms for energy minimization. For single-core processors, we propose a pseudo-polynomial algorithm based on dynamic programming that can achieve optimal solution. For multi-core processors, we propose a novel scheduling algorithm called SpringS which works like a spring and can effectively reduce energy consumption by iteratively adjusting task scheduling and voltage selection. We conduct experiments with a set of benchmarks from E3S (Dick 2008) and TGFF () based on the power model of the AMD Mobile Athlon4 DVS processor. The experimental results show that our technique can achieve 12.7% energy saving compared with the algorithms in Zhang et al. (2002) on average.

A Dynamic Voltage Scaling Algorithm for Dynamic Workloads

Dynamic Voltage Scaling (DVS) is a promising method to achieve energy saving by slowing down the processor into multiple frequency levels in battery-operated embedded systems. However, the worst case execution time (WCET) of the tasks scheduled by DVS must be known ahead of time to ensure their schedulability. In reality, a system’s workloads may change significantly without satisfying any prediction. In other words, a task’s WCET may not provide useful information about its future real execution time (RET). This paper presents a novel Dynamic-Mode EDF scheduling algorithm when workloads change significantly. One of the Single-Mode, Dual-Mode, and Three-Mode frequency setting formats can be applied, based on the RET and the accumulated slack at run-time. Only one combination of the number of modes/speeds, speed-switching transition points, and the frequency scaling factor for each mode can lead to the best energy saving. Experimental results show that, given an RET pattern, our Dynamic-Mode DVS algorithm achieves an average 15% energy savings over the traditional two-mode DVS scheme on hard real-time systems. Additionally, we also consider speed-switching or energy transition overhead, and implement a preliminary test of our proposed algorithm. With a less aggressive voltage scaling strategy (fewer speed changes for each job), deadlines can still be strictly satisfied and an average of 14% energy consumption saving over a non-DVS scheme is observed.

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。     

用于开放式系统的二维优先级实时调度

提出了一种新的用于开放式系统的调度机制,即二维优先级实时调度,它不仅划分任务优先级,还划分调度策略优先级.任务的执行顺序由其调度策略优先级和任务优先级共同决定.它不仅可以解决传统优先级调度机制中机制与调度策略不能相分离的问题,还提高了效率.这种机制中引入的CPU带宽控制策略,可以根据需要实现硬实时、软实时、混合实时不同目标的实时系统,并简化了任务可调度性分析,且可以为不同权限或级别的用户提供不同QoS服务.这种调度架构不仅效率高,而且具有很强的开放性,适用广、易扩展.     

Optimal Dynamic Voltage Scaling in Energy-Limited Nonpreemptive Systems with Real-Time Constraints

Dynamic voltage scaling is used in energy-limited systems as a means of conserving energy and prolonging their life. We consider a setting in which the tasks performed by such a system are nonpreemptive and aperiodic. Our objective is to control the processing rate over different tasks so as to minimize energy subject to hard real-time processing constraints. Under any given task scheduling policy, we prove that the optimal solution to the offline version of the problem can be efficiently obtained by exploiting the structure of optimal sample paths, leading to a new dynamic voltage scaling algorithm termed the critical task decomposition algorithm (CTDA). The efficiency of the algorithm rests on the existence of a set of critical tasks that decompose the optimal sample path into decoupled segments within which optimal processing times are easily determined. The algorithm is readily extended to an online version of the problem as well. Its worst-case complexity of both offline and online problems is O(N²)     

无线移动网络环境中混合式任务调度策略的研究与实现

无线移动网络具有性能不稳、易受外界干扰等特点,要求实时调度必须考虑网络变化影响.因为网络性能变化可直接导致网络延时变化,即任务耗费在网络传输上的时间发生变化,从而对任务集可调度性产生影响.传统调度算法大多未考虑网络因素.由此提出适应于网络性能不稳定环境下的混合式任务调度策略,测试结果表明该策略有利于提高混合实时任务在网络性能动态变化环境下的可调度性.     

树形异构网格的启发任务调度算法

讨论了在网格资源计算能力和网络通信速度异构的树形网格环境下任务调度问题,导出了线性方程并且根据调度任务大小进行了模型的优化,提出一个基于线性规划的任务分配启发式算法。实验结果表明:在异构树形计算网格环境下实现任务调度时,该算法的性能明显优于其他算法。