基于软实时多处理器系统的动态低功耗算法

结合DVS技术和(m,k)-firm模型，提出一个保证完成率、适用于软实时多电压多处理器系统中有依赖关系任务集的动态低功耗算法VAP_DY。该算法权衡应用的性能需求、执行时间的不确定性和系统对合理执行失败的容忍来动态调整每个处理器运行时的供电电压,以降低多处理器系统的总功耗。分析和实验结果表明，VAP_DY能够在保证时间和完成率约束的条件下有效降低系统功耗。     

多核系统中基于Global EDF 的在线节能实时调度算法

随着多核系统能耗问题日益突出,在满足时间约束条件下降低系统能耗成为多核实时节能调度研究中亟待解决的问题之一.现有研究成果基于事先已知实时任务属性的假设,而实际应用中,只有当任务到达之后才能够获得其属性.为此,针对一般任务模型,不基于任何先验知识提出一种多核系统中基于Global EDF在线节能硬实时任务调度算法,通过引入速度调节因子,利用松弛时间,结合动态功耗管理和动态电压/频率调节技术,降低多核系统中任务的执行速度,达到实时约束与能耗节余之间的合理折衷.所提出的算法仅在上下文切换和任务完成时进行动态电压/频率调节,计算复杂度小,易于在实时操作系统中实现.实验结果表明,该算法适用于不同类型的片上动态电压/频率调节技术,节能效果始终优于Global EDF算法,最多可节能15％～20％,最少可节能5％～10％.     

电池特性驱动的变电压任务调度

研究了变电压任务调度技术在电池能效优化领域中的应用，通过全面分析与电池能效相关的非线性特性，提出启发式的电池非线性特性驱动的变电压任务调度算法．该算法能有效地利用系统空闲时间优化电池放电电流分布，从而提高电池能效，降低目标任务执行消耗的能量．实验结果表明，该算法能够将执行目标任务消耗的能量降低30％以上，同时降低系统峰值功耗和平均功耗．     

功耗受限情况下多核处理器能效优化方案

将处理器功耗控制在预算以下有助于降低散热成本和提升系统稳定性,但现有功耗优化方案大多依赖线下分析得到的先验知识,影响实用性,而集中式搜索最优策略的算法也存在复杂度过高的问题。为此,提出功耗优化方案PPCM。利用动态电压频率调整(DVFS)技术控制CPU功耗在预算内以提高处理器能效。同时,将功耗控制和功耗分配解耦合以提高灵活性。采用动态调整的线性模型估计功耗,通过反馈控制技术对其进行调节。以计算访存比为指标在应用间分配功耗,并考虑多线程应用特征进行线程间功耗分配。实验结果表明,PPCM比Priority算法速度平均提高10.7%,能耗平均降低5.1%,能量-延迟积平均降低14.3%。与PCM CA算法相比,其速度平均提高4.5%,能量-延迟积平均降低5.0%。     

CPU频率对电池的影响以及对动态调压算法的改进

对电池供电的嵌入式系统来说，电池工作时间是重要的指标．利用动态调压算法对CPU频率进行调度．可以延长电池工作时间．然而，目前尚没有通用的方法对CPU频率和电池之间的关系进行深入研究，这导致动态调压算法不能最大限度地延长电池工作时间，本文提出一种新方法来研究CPU频率对电池的影响，其结论具有通用性，并得到实验的验证，利用这个结论，本文对动态调压算法进行了优化，使之更适于电池供电系统。     

高性能微处理器微体系结构级功耗模型及分析

基于Itanium2微处理器体系结构提出单时钟和多时钟域两种基准模型；对处理器的电路级特性进行微体系结构级抽象，建立了参数化的峰值功耗估算模型；提出事件调度算法，实现了多时钟域处理器系统的行为级模拟；以IMPACT工具集作为模拟引擎实现了处理器的动态功耗模拟模型．与其它同类模型Wattch相比，该模型能够支持多时钟系统的模拟，峰值功耗估算精度高了约3％，而模拟速度提高了42％．通过实验说明了多时钟域的功耗特性，在一种多电压和频率环境下，多时钟域处理器的功耗和能量分别降低了21％和38％．该模型可以很好地应用到体系结构级低功耗研究设计．     

SMS4密码算法的低功耗实现

根据实际应用场景,提出一种SMS4密码算法的低功耗实现方法。通过对前后分组加解密数据的密钥进行对比分析,有选择地对上一次操作结果进行复用,同时辅以动态时钟管理、操作数隔离、门控时钟等低功耗设计技术降低功耗。实验结果表明,该实现方法比原算法降低65%的功耗,等效门数减少13%。     

开销敏感的多处理器最优节能实时调度算法

嵌入式多处理器系统的能耗问题变得日益重要,如何减少能耗同时满足实时约束成为多处理器系统节能实时调度中的一个重要问题.目前绝大多数研究基于关键速度降低处理器的频率以减少动态能耗,采用关闭处理器的方法减少静态能耗.虽然这种方法可以实现节能,但是不能保证最小化能耗.而现有最优的节能实时调度未考虑处理器状态切换的时间和能量开销,因此在切换开销不可忽视的实际平台中不再是最优的.文中针对具有独立动态电压频率调节和动态功耗管理功能的多处理器系统,考虑处理器切换开销,提出一种基于帧任务模型的最优节能实时调度算法.该算法根据关键速度来判断系统负载情况,确定具有最低能耗值的活跃处理器个数,然后根据状态切换开销来确定最优调度序列.该算法允许实时任务在处理器之间任意迁移,计算复杂度小,易于实现.数学分析证明了该算法的最优性.     

功耗-体系结构描述语言XP-ADL及其设计环境

降低计算机系统的功耗日益成为系统设计中的重要目标，可配置VLIW体系结构在低功耗系统设计中具有显著的优势．本文提出一种功耗一体系结构描述语言XP-ADL，并介绍了基于该语言的体系结构设计环境．XP-ADL语言将系统中各功能部件的结构表示和它们的执行(功能)语义分离开来，方便了可配置的VLIW体系结构的描述．同时，为了便于在功耗模型下进行体系结构空间探索，XP-ADL允许在设计环境中包含各类功耗模型和功耗约束．     

分布多媒体系统的多媒体动态同步模型

在分布多媒体系统中，多媒体具有动态同步特性，同时关键媒体的同步要求在应用中享有优先权．现有的多媒体同步模型无法满足这些要求，现提出一个新的基于时间Ｐｅｔｒｉ网的多媒体动态同步模型——ＤＳＰＮ模型．它不仅能使分布多媒体系统依据实际运行状况和新提出的同步类型，以更准确、高效的方式来保持多媒体同步，而且允许用户对多媒体表现过程进行交互操作．     

Dynamic slack allocation algorithms for energy minimization on parallel machines

We explore novel algorithms for DVS (Dynamic Voltage Scaling) based energy minimization of DAG (Directed Acyclic Graph) based applications on parallel and distributed machines in dynamic environments. Static DVS algorithms for DAG execution use the estimated execution time. The estimated time in practice is overestimated or underestimated. Therefore, many tasks may be completed earlier or later than expected during the actual execution. For overestimation, the extra available slack can be added to future tasks so that energy requirements can be reduced. For underestimation, the increased time may cause the application to miss the deadline. Slack can be reduced for future tasks to reduce the possibility of not missing the deadline. In this paper, we present novel dynamic scheduling algorithms for reallocating the slack for future tasks to reduce energy and/or satisfy deadline constraints. Experimental results show that our algorithms are comparable to static algorithms applied at runtime in terms of energy minimization and deadline satisfaction, but require considerably smaller computational overhead.     

A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems

In this paper, we investigate the problem of scheduling precedence-constrained parallel applications on heterogeneous computing systems (HCSs) like cloud computing infrastructures. This kind of application was studied and used in many research works. Most of these works propose algorithms to minimize the completion time (makespan) without paying much attention to energy consumption.We propose a new parallel bi-objective hybrid genetic algorithm that takes into account, not only makespan, but also energy consumption. We particularly focus on the island parallel model and the multi-start parallel model. Our new method is based on dynamic voltage scaling (DVS) to minimize energy consumption.In terms of energy consumption, the obtained results show that our approach outperforms previous scheduling methods by a significant margin. In terms of completion time, the obtained schedules are also shorter than those of other algorithms. Furthermore, our study demonstrates the potential of DVS.     

基于DVS技术的性能感知反馈调度算法

传统DVS算法在能量管理方面没有考虑实际系统性能的需求,这在一定意义上限制了其节能效果．针对这一问题,提出一种基于DVS技术的性能感知反馈调度算法．在反馈调度器中,分别采用DVS技术和模糊控制技术设计CPU电压调节模块和控制任务周期调节模块,实现对系统CPU速率和控制任务采样周期的动态调节．通过与基于固定采样周期的DVS反馈调度算法进行对比,结果表明该算法在保证系统控制性能的同时进一步降低了系统能耗．     

基于DVS的实时多核嵌入式系统低功耗算法

动态电压调整（DVS）是低功耗设计方法中最基本的技术。然而,大部分的算法是基于单处理器平台的,并且仅考虑了相互独立的任务,这时使用DVS往往不能取得较好的效果。基于DVS提出了一种循环旋转调度技术来降低功耗,通过对程序中的循环进行重组,使得在满足时限的同时功耗最小,同时也考虑了电压转换所消耗的时间和功耗。     

An optimal speed control scheme supported by media servers for low-power multimedia applications

In this paper, we present a new concept of dynamic voltage scaling (DVS) for low-power multimedia decoding in battery-powered mobile devices. Most existing DVS techniques are suboptimal in achieving energy efficiency while providing the guaranteed playback quality of service, which is mainly due to the inherent limitations of client-only approaches. To address this problem, in this paper, we investigate the possibility of media server supported DVS techniques with smoothing mechanisms. Towards this new direction, we propose a generic offline bitstream analysis framework and an optimal speed control algorithm which achieves the maximal energy savings among all feasible speed profiles for the given buffers. The proposed scheme enables us to compute the buffer sizes of feasibility condition, which are the theoretical lower bound of buffer size requirement for a given media clip. More importantly, our scheme facilitates practical applications from four aspects. First, it does not require feedback information on clients’ configuration. This renders our scheme particularly suitable for broadcast or multicast applications. Second, the speed profile based on buffer sizes of feasibility condition can provide satisfactory energy efficiency. Third, the required buffer sizes are so small that they can be met by most mobile devices. Fourth, additional side information (i.e., speed profile) of the proposed scheme is negligible compared to the size of media content. These properties solve the diversity issue and feasibility issue of media server supported DVS schemes. Experimental results show that, in comparison with the representative existing techniques, our scheme improves the performance of DVS significantly.     

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.     

无线传感器网络中的动态电压调节算法

无线传感器节点一般由电池供电,且电池不易更换,所以传感器网络最关注的问题是如何高效地利用有限的能量.动态电压调节技术允许软件在运行时动态的改变处理器的频率和电压,以减少它的能量消耗.分析了现有的一些动态电压调节算法,根据无线传感器网络多跳路由和拓扑易变化的特点,提出了一种针对中继节点的动态电压调节算法,并在NS2平台上对算法进行了仿真.通过对仿真结果的分析,表明改进的算法能够很好地减少系统能量消耗,延长无线传感器网络的使用寿命.     

Reliability-aware low energy scheduling in real time systems with shared resources

Dynamic voltage scaling (DVS) is a technique which is widely used to save energy in a real time system. Recent research shows that it has a negative impact on the system reliability. In this paper, we consider the problem of the system reliability and focus on a periodic task set that the task instance shares resources. Firstly, we present a static low power scheduling algorithm for periodic tasks with shared resources called SLPSR which ignores the system reliability. Secondly, we prove that the problem of the reliability-aware low power scheduling for periodic tasks with shared resources is NP-hard and present two heuristic algorithms called SPF and LPF respectively. Finally, we present a dynamic low power scheduling algorithm for periodic tasks with shared resources called DLPSR to reclaim the dynamic slack time to save energy while preserving the system reliability. Experimental results show that the presented algorithm can reduce the energy consumption while improving the system reliability.     

Design and Performance Evaluation of Sequence Partition Algorithms

Tradeoffs between time complexities and solution optimalities are important when selecting algorithms for an NP-hard problem in different applications. Also, the distinction between theoretical upper bound and actual solution optimality for realistic instances of an NP-hard problem is a factor in selecting algorithms in practice. We consider the problem of partitioning a sequence of n distinct numbers into minimum number of monotone （increasing or decreasing） subsequences. This problem is NP-hard and the number of monotone subsequences can reach [√2n＋1/1-1/2]in the worst case. We introduce a new algorithm, the modified version of the Yehuda-Fogel algorithm, that computes a solution of no more than [√2n＋1/1-1/2]monotone subsequences in O（n^1.5） time. Then we perform a comparative experimental study on three algorithms, a known approximation algorithm of approximation ratio 1.71 and time complexity O（n^3）, a known greedy algorithm of time complexity O（n^1.5 log n）, and our new modified Yehuda-Fogel algorithm. Our results show that the solutions computed by the greedy algorithm and the modified Yehuda-Fogel algorithm are close to that computed by the approximation algorithm even though the theoretical worst-case error bounds of these two algorithms are not proved to be within a constant time of the optimal solution. Our study indicates that for practical use the greedy algorithm and the modified Yehuda-Fogel algorithm can be good choices if the running time is a major concern.