Adaptive workload driven dynamic power management for high performance computing clusters

With the scale expansion of high performance computer systems, efficient power management has developed into an important issue. To strive to balance power consumption and performance, this paper proposes an adaptive workload-driven dynamic power management policy for homogeneous clusters, which dynamically adjusts the power mode of computing nodes according to workload variation. The proposed policy combines the pre-wakeup method and the feedback mechanism to reduce performance degradation due to the wakeup delay. The experimental results demonstrate that, as compared with two existing timeout policies, adaptive workload-driven dynamic power management effectively reduced the performance loss with a slight increase in power consumption.     

基于工作周期优化的能量采集节点的功率控制

在无线传感网络WSNs(Wireless Sensor Networks)中,节点受到能量限制,因此,需通过功率管理技术有效地使用电池.而能量采集是补给电池能量的有效方式.为此,针对能量采集节点,提出功率控制算法,记为CLPM-PTPC(Close-Loop Power Manager-Predictive Transmission Power Controller).CLPM-PTPC算法是通过联合工作周期的优化和传输功率控制,实现节点功率的管理.CLPM-PTPC算法先利用闭环功率管理CLPM(Close-Loop Power Manager)调整传输任务的工作时期,再依据功率控制器PTPC(Predictive Transmission Power Controller)调整发射功率,使得传输功率能够实时地依据无线信道环境调整.实验数据表明,与基于固定传输功率算法相比,CLPM-PTPC算法的能量效率提高了约15％.     

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.     

Distributed Cooperative Control of Battery Energy Storage Systems in DC Microgrids

The control of battery energy storage systems(BESSs)plays an important role in the management of microgrids.In this paper,the problem of balancing the state-ofcharge(SoC)of the networked battery units in a BESS while meeting the total charging/discharging power requirement is formulated and solved as a distributed control problem.Conditions on the communication topology among the battery units are established under which a control law is designed for each battery unit to solve the control problem based on distributed average reference power estimators and distributed average unit state estimators.Two types of estimators are proposed.One achieves asymptotic estimation and the other achieves finite time estimation.We show that,under the proposed control laws,SoC balancing of all battery units is achieved and the total charging/discharging power of the BESS tracks the desired power.A simulation example is shown to verify the theoretical results.     

基于SoC的智能锂离子动力电池管理系统设计

针对目前锂离子动力电池保护电路扩展性和可靠性差的缺陷,设计了一种基于片上系统的锂离子动力电池保护电路,详细介绍了电路的硬件和软件设计,实现了灵活高效的锂离子动力电池管理方案.     

Learning policies for battery usage optimization in electric vehicles

The high cost, limited capacity, and long recharge time of batteries pose a number of obstacles for the widespread adoption of electric vehicles. Multi-battery systems that combine a standard battery with supercapacitors are currently one of the most promising ways to increase battery lifespan and reduce operating costs. However, their performance crucially depends on how they are designed and operated. In this paper, we formalize the problem of optimizing real-time energy management of multi-battery systems as a stochastic planning problem, and we propose a novel solution based on a combination of optimization, machine learning and data-mining techniques. We evaluate the performance of our intelligent energy management system on various large datasets of commuter trips crowdsourced in the United States. We show that our policy significantly outperforms the leading algorithms that were previously proposed as part of an open algorithmic challenge. Further, we show how to extend our approach to an incremental learning setting, where the policy is capable of improving and adapting as new data is being collected over time.     

SoC处理器的电源管理系统设计

从软硬件角度探讨SoC处理器电源管理系统的设计,分析SoC处理器PMU的特征,根据其应用需求讨论有关PMIC的设计问题,包括：电源IC内部结构及选型原则,数字PMIC与传统电源IC相比所做的改进,一种新的电源管理总线——PMBus和一种高度整合的PMIC应用。从嵌入式操作系统的角度分析了动态电源管理系统的设计。     

燃料电池有轨电车能量管理Pareto多目标优化

节能环保的出行方式得到政府的大力推广, 其中燃料电池混合动力有轨电车由于可无网运行且节能环保而备受关注.为了改善燃料电池/超级电容/动力电池大功率有轨电车的燃料经济性与系统耐久性, 提出一种有轨电车能量管理策略(Energy management strategy, EMS)的多目标优化方法. 首先以氢燃料消耗量和能量源性能衰减率作为评价指标, 建立多目标成本函数. 由于两个指标很难在同一个等式中评价, 设计了基于状态机与非支配排序的能量管理Pareto多目标优化方法, 获得了有轨电车能量管理策略Pareto非劣解集, 并分析了能量管理策略的目标功率参数对性能指标的影响规律, 进而遴选出兼顾燃料经济性与系统耐久性的综合最优解. 结果表明, 与功率跟随策略和基于遗传算法优化策略相比, 该能量管理优化方法的燃料经济性分别提高了29.4 %和2.4 %.     

SAFEPOWER project: Architecture for safe and power-efficient mixed-criticality systems

With the ever increasing industrial demand for bigger, faster and more efficient systems, a growing number of cores is integrated on a single chip. Additionally, their performance is further maximized by simultaneously executing as many processes as possible without regarding their criticality. Even safety critical domains like railway and avionics apply these paradigms under strict certification regulations. As the number of cores is continuously expanding, the importance of cost-effectiveness grows. One way to increase the cost-efficiency of such System on Chip (SoC) is to enhance the way the SoC handles its power resources. By increasing the power efficiency, the reliability of the SoC is raised because the lifetime of the battery lengthens. Secondly, by having less energy consumed, the emitted heat is reduced in the SoC which translates into fewer cooling devices. Though energy efficiency has been thoroughly researched, there is no application of those power saving methods in safety critical domains yet. The EU project SAFEPOWER¹ targets this research gap and aims to introduce certifiable methods to improve the power efficiency of mixed-criticality real-time systems (MCRTES). This article will introduce the requirements that a power efficient SoC has to meet and the challenges such a SoC has to overcome.     

Diagnosis of feedwater heater performance degradation using fuzzy inference system

Power generation facilities cannot avoid performance degradation caused by severe operating conditions such as high temperature and high pressure, as well as the aging of facilities. Since the performance degradation of facilities can inflict economic on power generation plants, a systematic method is required to accurately diagnose the conditions of the facilities.This paper introduces the fuzzy inference system, which applies fuzzy theory in order to diagnose performance degradation in feedwater heaters among power generation facilities. The reason for selecting only feedwater heaters as the object of analysis is that it plays an important role in the performance degradation of power generation plants, which have recently been reported with failures. In addition, feedwater heaters have the advantage of using many data types that can be used in fuzzy inference because of low measurement limits compared to other facilities. Fuzzy inference systems consists of fuzzy sets and rules with linguistic variables based on expert knowledge, experience and simulation results to efficiently handle various uncertainties of the target facility. We proposed a method for establishing a more elaborate system. According to the experimental results, inference can be made with consideration on uncertainties by quantifying the target based on fuzzy theory. Based on this study, implementation of a fuzzy inference system for diagnosis of feedwater heater performance degradation is expected to contribute to the efficient management of power generation plants.     

面向UMPC的北大众志-SK系统芯片设计

如何更好地满足3C融合的需求,是超便携个人计算机(UMPC)普及的关键.北大众志-SK系统芯片,将传统个人计算机中分布在主板上的中央处理器、北桥与南桥芯片组、显示控制器和其它输入输出控制设备等众多芯片的功能集成到单一芯片中.该系统芯片采用2D/3D扩展指令、软硬协同视频解码加速部件、硬件视频编解码等方式,在高效完成多媒体处理的前提下,有效降低了对中央处理器性能的需求.通过在单芯片内部实现多层次的存储架构,简化了数据的传输路径,提高了数据传输的效率,从而提高系统性能.此外,在该系统芯片中还实现了众多主流的输入输出接口控制部件,以满足个人计算机的日常应用需求.该设计达到了高集成度、高性能、低功耗的设计目标,提供了面向教育、电子政务和个人信息处理等领域的低成本、低功耗、易使用、便于维护的UMPC解决方案.     

Android智能手机平台电源管理技术

随着智能手机的普及发展,对系统的功能、可靠性、成本、体积、功耗提出了更严格的要求,特别是电池供电系统,在电池性能严重滞后于手持终端设备需求的背景下,电源管理技术正成为这些产品设计的关键所在。在满足功能实现的前提下,使手机待机时间更长的电源管理技术已成为研究热点。通过分析目前流行的android智能手机电源管理技术,探讨其现状并研究其应用前景。     

数据中心机房蓄电池的安全运行应用

阀控密封式铅酸蓄电池(以下简称机房蓄电池)应用非常广泛,更是大量应用于各行业的数据中心机房,这些机房蓄电池是机房电源系统中非常重要的组成部分,是机房电源维护人员非常关注的电源设备之一,是维护工作的重中之重。从机房蓄电池在实际使用中的维护和管理的角度出发,本文对影响蓄电池寿命的因素进行了分析,运用机房蓄电池在线检测和维护系统,对蓄电池实时状态监测;诊断蓄电池好与坏,确保蓄电池能够正常运行;通过均衡维护方式,大大提高了蓄电池的使用寿命。并且通过远端集中监控平台进行蓄电池的集中管理,以更好地落实"安全第一、预防第一、综合治理"的安全生产方针,提高维护水平,缓解日益紧张的人员及维护工作的压力。     

快速平均一致与多采样率下储能系统精准功率分配

针对智能电网中储能系统分布式功率分配问题, 本文提出融合了快速平均一致与多采样率的离散时间控制策略, 使各储能电池以相同相对剩余电量状态(SoC)变化率进行充放电. 为了仅用局部通信得到计算功率分配值需要的全局平均值, 在储能电池拓扑结构已知和未知两种情况下, 本文分别采用了有限时间平均一致算法和具有最优收敛率的平均一致算法. 在储能电池单个控制周期内, 平均一致算法以小于电池控制周期的采样周期进行多步迭代, 从而得到精确的估计值, 实现各储能电池的精准功率分配. 不同于传统连续时间控制方法仅给出平均一致算法的收敛性分析, 本文不仅建立了单个控制周期内快速平均一致算法的收敛率表达式, 而且给出了包含储能电池动力学和平均一致算法的整个控制系统的渐近稳定性分析. 所提方法能够更快速、更精准地实现基于相同相对SoC变化率的功率分配. 最后, 本文通过多组仿真实验验证了所提方法的有效性.     

面向深度学习的SoC架构设计与仿真

互联网时代信息量的爆炸式增长、深度学习的普及使传统通用计算无法适应大规模、高并发的计算需求。异构计算能够为深度学习释放更强的计算能力，达到更高的性能要求，并可应用于更广阔的计算场景。针对深度学习算法，设计仿真了一款完整的异构计算SoC架构。首先，通过对常用深度学习算法，如GoogleNet、LSTM、SSD，进行计算特征分析，将其归纳为有限个共性算子类，并用图表及结构框图的形式进行展示，同时生成最小算子级别伪指令流。其次，根据提取的算法特征，进行面向深度学习的硬件加速AI IP核设计，构建异构计算SoC架构。最后，通过仿真建模平台进行实验验证，SoC系统的性能功耗比大于1.5 TOPS/W,可通过GoogleNet算法对10路1 080 p 30 fps视频逐帧处理，且每帧端到端的处理时间不超过30 ms。     

Optimal Constrained Self-learning Battery Sequential Management in Microgrid Via Adaptive Dynamic Programming

This paper concerns a novel optimal self-learning battery sequential control scheme for smart home energy systems. The main idea is to use the adaptive dynamic programming (ADP) technique to obtain the optimal battery sequential control iteratively. First, the battery energy management system model is established, where the power efficiency of the battery is considered. Next, considering the power constraints of the battery, a new non-quadratic form performance index function is established, which guarantees that the value of the iterative control law cannot exceed the maximum charging/discharging power of the battery to extend the service life of the battery. Then, the convergence properties of the iterative ADP algorithm are analyzed, which guarantees that the iterative value function and the iterative control law both reach the optimums. Finally, simulation and comparison results are given to illustrate the performance of the presented method.      

Model-driven coordinated management of data centers

Management of computing infrastructure in data centers is an important and challenging problem, that needs to: (i) ensure availability of services conforming to the Service Level Agreements (SLAs); and (ii) reduce the Power Usage Effectiveness (PUE), i.e. the ratio of total power, up to half of which is attributed to data center cooling, over the computing power to service the workloads. The cooling energy consumption can be reduced by allowing higher-than-usual thermostat set temperatures while maintaining the ambient temperature in the data center room within manufacturer-specified server redline temperatures for their reliable operations. This paper proposes: (i) a Coordinated Job, Power, and Cooling Management (JPCM) policy, which performs: (a) job management so as to allow for an increase in the thermostat setting of the cooling unit while meeting the SLA requirements, (b) power management to reduce the produced thermal load, and (c) cooling management to dynamically adjust the thermostat setting; and (ii) a Model-driven coordinated Management Architecture (MMA), which uses a state-based model to dynamically decide the correct management policy to handle events, such as new workload arrival or failure of a cooling unit, that can trigger an increase in the ambient temperature. Each event is associated with a time window, referred to as the window-of-opportunity, after which the temperature at the inlet of one or more servers can go beyond the redline temperature if proper management policies are not enforced.This window-of-opportunity monotonically decreases with increase in the incoming workload. The selection of the management policy depends on their potential energy benefits and the conformance of the delays in their actuation to the window-of-opportunity. Simulations based on actual job traces from the ASU HPC data center show that the JPCM can achieve up to 18% energy-savings over separated power or job management policies. However, high delay to reach a stable ambient temperature (in case of cooling management through dynamic thermostat setting) can violate the server redline temperatures. A management decision chart is developed as part of MMA to autonomically employ the management policy with maximum energy-savings without violating the window-of-opportunity, and hence the redline temperatures. Further, a prototype of the JPCM is developed by configuring the widely used Moab cluster manager to dynamically change the server priorities for job assignment.     

State of charge estimation for lithium-ion batteries: An adaptive approach

State of charge (SoC) estimation is of key importance in the design of battery management systems. An adaptive SoC estimator, which is named AdaptSoC, is developed in this paper. It is able to estimate the SoC in real time when the model parameters are unknown, via joint state (SoC) and parameter estimation. The AdaptSoC algorithm is designed on the basis of three procedures. First, a reduced-complexity battery model in state-space form is developed from the well-known single particle model (SPM). Then a joint local observability/identifiability analysis of the SoC and the unknown model parameters is performed. Finally, the SoC is estimated simultaneously with the parameters using the iterated extended Kalman filter (IEKF). Simulation and experimental results exhibit the effectiveness of the AdaptSoC.     

无线网络节点功率控制仿真研究

在WNCS(无线网络控制系统)仿真中节点的功率控制是仿真关键问题之一。为了研究节点的功率控制问题,基于Truetime工具箱中的Kernel模块和Wireless Network模块,首先,提出了两种功率控制策略,一种是把网络控制中RM(RateMonotonic)调度策略的思想应用到节点功率的控制和调度中,而提出的PM(Power Monotonic)功率控制策略,另一种是基于TrueTime仿真示例通过归纳而提出的AH功率控制策略,并着重介绍了PM功率控制策略的算法。最后通过一个无线网络控制系统的仿真模型,就这两种节点功率控制策略对系统性能的影响进行仿真验证。     

Hybrid power management in real time embedded systems: an interplay of DVFS and DPM techniques

Energy-aware scheduling of real time applications over multiprocessor systems is considered in this paper. Early research reports that while various energy-saving policies, for instance Dynamic Power Management (DPM) and Dynamic Voltage & Frequency scaling (DVFS) policies, perform well individually for a specific set of operating conditions, they often outperform each other under different workload and/or architecture configuration. Thus, no single policy fits perfectly all operating conditions. Instead of designing new policies for specific operating conditions, this paper proposes a generic power/energy management scheme that takes a set of well-known existing (DPM and DVFS) policies, each of which performs well for a set of conditions, and adapts at runtime to the best-performing policy for any given workload. Experiments are performed using state-of the-art DPM and DVFS policies and the results show that our proposed scheme adapts well to the changing workload and always achieves overall energy savings comparable to that of best-performing policy at any point in time.