基于SSD数据库负载的SQL能耗感知模型

面对大数据带来的能耗及环境方面的严峻问题，构建节能的绿色数据库系统已成为关键需求和重要挑战。针对现有数据库系统主要以性能优化为目标，缺少对能耗的感知及优化的问题，提出基于数据库负载的能耗感知模型，并将模型应用于基于固态硬盘（SSD）的数据库系统中。首先，将数据库负载执行过程中对主要系统资源（CPU、固态硬盘）的消耗解析为时间开销和功耗开销，并基于SSD数据库负载的基本I/O类型构建时间开销模型和功耗开销模型，实现为数据库构建资源开销单位统一的能耗感知模型；然后，利用多元线性回归实现对模型的求解，并分别在独占环境和竞争环境下，验证模型对不同I/O类型的数据库负载能耗估算的准确性；最后，分析实验结果，并讨论了影响模型准确性的因素。经实验验证模型准确度较高，在DBMS独占系统资源情况下的平均误差为5.15%，绝对误差不超过9.8%；竞争环境下的准确率相对下降，但平均误差也低于12.21%，可有效构建能耗感知的绿色数据库系统。     

Energy consumption analysis of data stream processing: a benchmarking approach

Energy efficiency of data analysis systems has become a very important issue in recent times because of the increasing costs of data center operations. Although distributed streaming workloads have increasingly been present in modern data centers, energy‐efficient scheduling of such applications remains as a significant challenge. In this paper, we conduct an energy consumption analysis of data stream processing systems in order to identify their energy consumption patterns. We follow stream system benchmarking approach to solve this issue. Specifically, we implement Linear Road benchmark on six stream processing environments (S4, Storm, ActiveMQ, Esper, Kafka, and Spark Streaming) and characterize these systems' performance on a real‐world data center. We study the energy consumption characteristics of each system with varying number of roads as well as with different types of component layouts. We also use a microbenchmark to capture raw energy consumption characteristics. We observed that S4, Esper, and Spark Streaming environments had highest average energy consumption efficiencies compared with the other systems. Using a neural networkbased technique with the power/performance information gathered from our experiments, we developed a model for the power consumption behavior of a streaming environment. We observed that energy‐efficient execution of streaming application cannot be specifically attributed to the system CPU usage. We observed that communication between compute nodes with moderate tuple sizes and scheduling plans with balanced system overhead produces better power consumption behaviors in the context of data stream processing systems. Copyright © 2016 John Wiley & Sons, Ltd.     

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.     

基于NDIS和层次I/O的组合网关实现技术剖析

介绍了NDIS、层次I/O及其在Windows网络协议栈实现中的地位和作用,剖析了采用核心态NDIS协议驱动程序和伪设备驱动程序,在多个协议栈层次上进行不同处理的组合网关实现技术,给出了使用这项技术的基本方法,评估了它的性能.     

基于数据库负载的SQL能耗预测模型

传统数据库以性能(吞吐量、响应时间)为首要优化目标,忽略了数据库系统的能量消耗。在一味追求性能的同时,高能耗问题日益突出,为数据库负载构建能耗模型是构建绿色数据库的基础。通过量化查询负载执行过程中对系统资源(CPU与磁盘)的消耗,将资源消耗产生的时间代价和功耗代价转化为时间代价预测模型和功率代价预测模型,在单站点数据库服务器上实现了为数据库系统构建资源单位代价统一的能耗预测模型。采用多元线性回归工具拟合模型的重要参数,实验结果验证了能耗预测模型的可行性;并分别在静态与动态的系统环境下对系统不同类型查询负载的能耗进行预测与评价,验证了该模型的准确性,使得提出的能耗模型适合于构建能耗感知的绿色数据库。     

Toward low CPU usage and efficient DPDK communication in a cluster

In recent years, DPDK (Data Plane Development Kit, a data plane development tool set provided by Intel, focusing on high-performance processing of data packets in network applications), one of the high-performance packet I/O frameworks, is widely used to improve the efficiency of data transmission in the cluster. But, the busy polling used in DPDK will not only waste a lot of CPU cycles and cause certain power consumption, but also the high CPU usage will have a great impact on the performance of other applications in the host. Although some technologies, such as DVFS (dynamic voltage and frequency scaling, which is to dynamically adjust the operating frequency and voltage of the chip according to the different needs of the computing power of the application running on the chip, so as to achieve the purpose of energy saving) and LPI (low power idle, a technology that saves power by turning off the power of certain supporting circuits when the CPU core is idle), can reduce power consumption by adjusting CPU voltage and frequency, they can also cause performance degradation in other applications. Using thread sleep technology is a promising method to reduce the CPU usage and power consumption. However, it is challenging because the appropriate thread sleep duration cannot be obtained accurately. In this paper, we propose a model that finds the optimal thread sleep duration to solve the above challenges. From the model, we can balance the thread CPU usage and transmission efficiency to obtain the optimal sleep duration called the transmission performance threshold. Experiments show that the proposed models can significantly reduce the thread CPU usage. Generally, while the communication performance is slightly reduced, the CPU utilization is reduced by about 80%.

     

Deconstructing Network Attached Storage systems

Network Attached Storage (NAS) has been gaining general acceptance, because it can be managed easily and files shared among many clients, which run different operating systems. The advent of Gigabit Ethernet and high speed transport protocols further facilitates the wide adoption of NAS. A distinct feature of NAS is that NAS involves both network I/O and file I/O. This paper analyzes the layered architecture of a typical NAS and the data flow, which travels through the layers. Several benchmarks are employed to explore the overhead involved in the layered NAS architecture and to identify system bottlenecks. The test results indicate that a Gigabit network is the system bottleneck due to the performance disparity between the storage stack and the network stack. The tests also demonstrate that the performance of NAS has lagged far behind that of the local storage subsystem, and the CPU utilization is not as high as imagined. The analysis in this paper gives three implications for the NAS, which adopts a Gigabit network: (1) The most effective method to alleviate the network bottleneck is increasing the physical network bandwidth or improving the utilization of network. For example, a more efficient network file system could boost the NAS performance. (2) It is unnecessary to employ specific hardware to increase the performance of the storage subsystem or the efficiency of the network stack because the hardware cannot contribute to the overall performance improvement. On the contrary, the hardware methods could have side effect on the throughput due to the small file accesses in NAS. (3) Adding more disk drives to an NAS when the aggregate performance reaches the saturation point can only contribute to storage capacity, but not performance. This paper aims to guide NAS designers or administrators to better understand and achieve a cost-effective NAS.     

低CPU开销的低延迟存储引擎

近些年来,固态存储的硬件处理速率得到了极大改善.一块超低延迟的固态存储盘能在10μs内处理4KB大小的数据.加速I/O收割过程以构建低延迟的存储引擎是存储系统研究中的一个重要研究课题.传统存储系统通过硬件中断机制收割I/O,却引入了额外的上下文切换开销,延长了整体I/O处理时间.现有工作使用轮询机制以消除上下文切换,却...     

基于嵌入式软件算法级能耗问题研究

能耗已经成为嵌入式系统设计中一个重要的约束条件.嵌入式系统是典型的软件驱动执行系统,硬件的电路活动直接导致系统参数功耗,而软件中的指令执行和数据存取等操作底层的微处理、总线、Cache、存储器和I/O接口等硬件的活动都会间接的导致系统参数功耗.在现代“低碳经济”的背景下嵌入系统的功耗已经引起人们关注的一个重点.而软件设计早期对高层所作的功率耗评估和优化对整个系统的的能耗影响最为显著.本文通过构建算法级能耗估算模型,并通过实例采用神经网络算法、遗传算法等进行能耗求解,同时在求解过程中进行能耗分析.     

基于ISA总线的USB协议栈的设计与实现

USB已经成为一种事实上的I／O标准，本文以自主开发的基于ISA总线的USB为例，介绍了该USB的硬件研发过程及相关系统软件的开发过程，详尽地展示了相关USB协议栈的设计与实现，并且探讨了USB设备基于该协议栈的驱动开发．     

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.     

High performance network virtualization with SR-IOV

Virtualization poses new challenges to I/O performance. The single-root I/O virtualization (SR-IOV) standard allows an I/O device to be shared by multiple Virtual Machines (VMs), without losing performance. We propose a generic virtualization architecture for SR-IOV-capable devices, which can be implemented on multiple Virtual Machine Monitors (VMMs). With the support of our architecture, the SR-IOV-capable device driver is highly portable and agnostic of the underlying VMM. Because the Virtual Function (VF) driver with SR-IOV architecture sticks to hardware and poses a challenge to VM migration, we also propose a dynamic network interface switching (DNIS) scheme to address the migration challenge. Based on our first implementation of the network device driver, we deployed several optimizations to reduce virtualization overhead. Then, we conducted comprehensive experiments to evaluate SR-IOV performance. The results show that SR-IOV can achieve a line rate throughput (9.48 Gbps) and scale network up to 60 VMs, at the cost of only 1.76% additional CPU overhead per VM, without sacrificing throughput and migration.     

Exploring optimal combination of a file system and an I/O scheduler for underlying solid state disks

Performance and energy consumption of a solid state disk （SSD） highly depend on file systems and I/O schedulers in operating systems. To find an optimal combination of a file system and an I/O scheduler for SSDs, we use a metric called the aggregative indicator （AI）, which is the ratio of SSD performance value （e.g., data transfer rate in MB/s or throughput in IOPS） to that of energy consumption for an SSD. This metric aims to evaluate SSD performance per energy consumption and to study the SSD which delivers high performance at low energy consumption in a combination of a file system and an I/O scheduler. We also propose a metric called Cemp to study the changes of energy consumption and mean performance for an Intel SSD （SSD-I） when it provides the largest AI, lowest power, and highest performance, respectively. Using Cemp, we attempt to find the combination of a file system and an I/O scheduler to make SSD-I deliver a smooth change in energy consumption. We employ Filebench as a workload generator to simulate a wide range of workloads （i.e., varmail, fileserver, and webserver）, and explore optimM combinations of file systems and I/O schedulers （i.e., optimal values of AI） for tested SSDs under different workloads. Experimental results reveal that the proposed aggregative indicator is comprehensive for exploring the optimal combination of a file system and an I/O scheduler for SSDs, compared with an individual metric.     

一种优化MapReduce系统能耗的数据布局算法

在云计算技术和大数据技术的推动下,IT资源的规模不断扩大,其能耗问题日益显著.研究表明:节点资源利用率不高、资源空闲导致的能源浪费,是目前大规模分布式系统的主要问题之一.研究了MapReduce系统的能耗优化.传统的基于软件技术的能耗优化方法多采用负载集中和节点开关算法,但由于MapReduce任务的特点,集群节点不仅要完成运算,还需要存储数据,因此,传统方法难以应用到MapReduce集群.提出了良好的数据布局可以优化集群能耗.基于此,首先定义了数据布局的能耗优化目标,并提出相应的数据布局算法;接着,从理论上证明该算法能够实现数据布局的能耗优化目标;最后,在异构集群中部署3种数据布局不同的MapReduce系统,通过对比三者在执行CPU密集型、I/O密集型和交互型这3种典型运算时的集群能耗,验证了所提出的数据布局算法的能耗优化效果.理论和实验结果均表明,所提出的布局算法能够有效地降低MapReduce集群的能耗.上述工作都将促进高能耗计算和大数据分析的应用.     

基于OPC的第三方设备数据采集系统的设计与实现

在当前工业控制系统中,DCS系统与第三方设备进行通信时,必须开发对应的设备驱动程序;当DCS系统升级时,设备驱动也必须进行相应升级,从而增加了升级和维护成本。为此,提出了基于OPC的第三方设备数据采集系统(COMMOPC系统)。该系统由主框架和I/O驱动组成,I/0驱动为主框架提供统一的通信调用接口,主框架部分通过接口的调用来实现对设备I/0的统一管理、调度和通信信息的集中监视,并提供通用OPC Server接口,使上层应用系统通过OPC Client可实时地访问现场设备,解决了设备驱动程序与DCS系统必须一一对应的问题。     

基于ZigBee和C#的农田数据采集系统

针对农田数据采集系统有线传输方式常受到地形、安装环境等限制问题,提出了一种基于ZigBee数据采集传输系统.采用CC2530芯片为主搭建无线传感器网络,终端和协调器采用半开源的Z-Stack协议栈进行程序开发,上位机基于Visio Studio 2015平台采用C#语言进行软件开发,并配合ACCESS数据库,共同实现了系统的远程检测与本地保存和动态显示.在玉米试验田进行实地测试,终端按采集需要暂时设定为每间隔1 h采集一次数据,其余时间处于休眠状态.经计算,终端预计工作时间为100 d左右;且经过测试,终端与协调器的有效传输距离能达到80 m,数据传输有效率达92%.结果表明:该系统具有低功耗、低成本、系统运行稳定、可扩展性强等特点,满足农业信息化的需求.     

SQL能耗建模及优化研究

IT系统能耗的节节攀升,使得设计新一代DBMS时必须考虑其能耗效率问题。由于SQL语句的执行过程大约消耗70%~90%的数据库资源,因此对SQL进行能耗建模及优化对提高数据库的能源使用效率具有重要的意义。在对SQL查询处理机制进行研究的基础上,构建了SQL能耗模型,并对一系列查询优化原则进行了实验,以表明不同优化原则对性能提升及能耗减少的有效性。实验及能耗数据分析表明:CPU利用率是影响系统功耗的最关键因素,SQL能耗优化方法可忽略内存优化且应该均衡考虑性能优化及功耗优化两方面,提出的SQL能耗模型及节能优化方法具有较强的应用价值。     

A novel non-volatile memory storage system for I/O-intensive applications

The emerging memory technologies, such as phase change memory (PCM), provide chances for highperformance storage of I/O-intensive applications. However, traditional software stack and hardware architecture need to be optimized to enhance I/O efficiency. In addition, narrowing the distance between computation and storage reduces the number of I/O requests and has become a popular research direction. This paper presents a novel PCMbased storage system. It consists of the in-storage processing enabled file system (ISPFS) and the configurable parallel computation fabric in storage, which is called an in-storage processing (ISP) engine. On one hand, ISPFS takes full advantage of non-volatile memory (NVM)’s characteristics, and reduces software overhead and data copies to provide low-latency high-performance random access. On the other hand, ISPFS passes ISP instructions through a command file and invokes the ISP engine to deal with I/O-intensive tasks. Extensive experiments are performed on the prototype system. The results indicate that ISPFS achieves 2 to 10 times throughput compared to EXT4. Our ISP solution also reduces the number of I/O requests by 97% and is 19 times more efficient than software implementation for I/O-intensive applications.     

一种基于MBRC值的关系型数据库负载能耗预测模型

数据库负载的能耗解析与建模是构建节能的绿色数据库的基础。针对数据库负载的高能耗问题,将SQL语句消耗的系统资源(CPU和磁盘)映射为时间代价与功率代价,为数据库负载构建能耗预测模型。首先,根据负载的系统资源消耗模式,计算负载的功耗代价;然后,根据负载资源消耗产生的时间代价,为负载构建动态能耗预测模型;最后,利用MBRC值的设置对预测模型的准确度进行深入的研究。实验结果表明,所构建的预测模型能够对数据库负载的能量消耗进行较准确的预测,预测模型的准确度研究有助于在不同的系统环境配置下提升动态能耗预测模型的稳定性与精确度。     

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.