IT系统一体化运维监控及服务预警平台的设计与实现

本文介绍了大型企业IT系统运维监控的背景和难点,提出了IT系统一体化运维监控的技术架构和功能架构,从而实现了企业IT系统事件级的集成以及面向业务流程的监控和服务预警。     

基于LabVIEW的特种车底盘监控系统设计

目前特种车从传统的内燃机驱动向电驱动发展,底盘控制基于CAN总线通信I,实现各系统内及系统间的数据交互,在底盘研制过程的总调阶段及售后的维护阶段,为方便系统调试和诊断系统的健康状态,基于LabVIEW设计了一套监控系统,该系统可实时采集显示CAN总线上各分系统的状态数据,并可发送指令控制各分系统的执行机构,同时可对各分系统的传感器进行标定。本文详细描述了特种车底盘的网段设计和通信协议,介绍了监控系统的组成和监控软件的具体实现,并通过实际应用验证了监控系统各项功能,效果良好。     

船载“动中通”伺服系统控制电路的硬件设计

船载"动中通"地球站的整个硬件系统的组成包括伺服分系统、天馈分系统和监控分系统三大分系统。本设计主要完成的工作是以STM32为基础,实现对船载"动中通"地球站下位机伺服系统的硬件控制电路的研究与设计。以硬件设计为主线,介绍了"动中通"伺服系统的控制电路具体的设计和实现,包括硬件的选型和电路图的设计。     

面向电力监控系统安全运维的主机加固技术研究与设计

随着《中华人民共和国网络安全法》、《关键信息基础设施安全保护条例》等有关法律法规的出台，对于电力系统的网络安全的要求日益严格，做好电力专业系统的网络安全隐患排查整改刻不容缓。而在电力监控系统的传统安全运维过程中，过度依赖运维人员经验，不合规配置、弱口令、高危端口开放等威胁因素广泛存在于电力监控系统主机中，对电力系统的稳定、安全运行带来极大的挑战。本文对电力监控系统主机设备潜在的可利用威胁进行分析，结合安全运维工作智能化的总体趋势，研究针对电力监控系统高价值主机的加固技术，并对主机自动化加固工具进行设计。减少电力监控系统的潜在危害，以提升电力系统应对网络安全威胁的健壮性和鲁棒性。     

政府IT运维管理信息平台的构建

按照ITIL的管理理念,结合我国政府部门IT运维存在的问题,总结分析了在政府部门实施ITIL及建立IT管理体系所面临的挑战,提出了基于ITIL理念的政府IT运维管理信息平台的总体架构,并就集中监控系统、基础配置系统、运维服务台、运维流程管理系统、综合分析系统、知识库的功能及相互关系进行了论证。     

基于无线组网的分布式光伏监控系统设计及应用

针对分布式光伏监控系统通信线缆敷设复杂且成本高、设备运行状态监控手段单一、受地形影响运维效率低等问题,考虑了分布式农业光伏实际建设场景,并对分布式光伏项目概况和监控需求进行了调研分析。在此基础上,设计了基于无线组网的分布式光伏监控系统。系统包括电站层、通信层、主站层3部分。以时间维度为基础,从发电空间管控、发电过程管控、运维执行管控和事后评价4个方面出发,建立了涵盖资源信息统计、发电能力预测、电站运行监控、运营特性分析的监控策略。结合系统在广东湛江市遂溪县光伏农业综合利用项目中的应用,表明应用该系统后光伏电站年发电能力提升约1.2%,运维成本整体降低。该设计具有一定的推广价值。     

基于InfluxDB的监控设备通用运维管理平台

监控设备大规模安装和应用带来了运维困难的问题.本文研发了一个通用运维管理平台,平台通过前端状态采集装置获取监控设备运行时产生的各种状态信息;平台的运维管理系统汇总、存储状态信息,并提供监控、统计、分析、报警等管理功能以支持运维业务;平台的可视化展示子系统对状态信息进行可视化渲染呈现,为系统运维提供智能化的支持.状态采集装置模块化和冗余设计使得设备具有通用性、可扩展性、高可靠性和易维护性.状态信息具有时序数据特征,运维管理系统使用InfluxDB存储状态信息,大幅度降低了存储空间需求,并保证了数据查询和管理的性能需求.基于InfluxDB的监控设备通用运维管理平台已在多个用户单位安装试用,运行状态良好,具有很好的经济社会效益和推广价值.     

一种基于Hadoop的云运维监控模型设计与实现

随着企业信息化在生产实时监测、海量存储和科学分析决策等方面的需求不断提升,运维监控系统已逐渐成为主要的管理手段。采用最新的云计算技术,设计及搭建一个数据规模易扩展、处理速度快、安全性高、成本低的云运维监控系统;针对运维控制系统中海量监控历史数据实时提取响应速度慢的缺点,设计并实现一种基于Hadoop的分布式海量数据处理模型。仿真实验证明,Hadoop在对云监控系统中的海量数据提取效率优于传统方法,随着数据量的快速增长,优势越明显。     

可重复使用飞行器地面系统超融合设计及应用

针对可重复使用飞行器地面系统应用要求和现行方案存在的问题,改变各分系统独立设计思路,将地面系统作为一个整体,将其广义的各类计算机抽取出来,进行超融合设计,通过软件定义硬件和服务器虚拟化、存储虚拟化、网络虚拟化、显控桌面化,可实现根据各分系统任务需要,进行计算机资源的灵活分配和动态调度,以虚拟机、云桌面的形式供各分系统使用,简化了地面系统的配置和部署,实现了地面系统的通用化、简易化、便捷化、可配置化、高可用性;与各分系统独立设计方案应用对比和性能测试表明,地面系统超融合设计减少了IT设施32%,缩短了部署时间52%,降低了运维复杂度60%,可用性达到了99.9%以上,服务延迟率低于44%,并可实现灵活配置,能适应不同型号的可重复使用飞行器应用。     

智能交通一体化运维管理系统设计与实践

针对国内各大中城市智能交通系统建设过程中所出现的大量智能交通系统和数据运维管理问题,提出了一种基于最新ITIL架构,整合智能监控、RFID、PGIS等最新理论和技术,应用于智能交通系统一体化运维管理方面的设计与实现方法,旨在将多个子系统无缝集成.系统采用运维门户层、系统管理层、监控管理层、数据统一汇聚管理四大模块.目前该系统已在交通管理部门得到应用.     

Prediction of job characteristics for intelligent resource allocation in HPC systems: a survey and future directions

Nowadays, high-performance computing (HPC) clusters are increasingly popular. Large volumes of job logs recording many years of operation traces have been accumulated. In the same time, the HPC cloud makes it possible to access HPC services remotely. For executing applications, both HPC end-users and cloud users need to request specific resources for different workloads by themselves. As users are usually not familiar with the hardware details and software layers, as well as the performance behavior of the underlying HPC systems. It is hard for them to select optimal resource configurations in terms of performance, cost, and energy efficiency. Hence, how to provide on-demand services with intelligent resource allocation is a critical issue in the HPC community. Prediction of job characteristics plays a key role for intelligent resource allocation. This paper presents a survey of the existing work and future directions for prediction of job characteristics for intelligent resource allocation in HPC systems. We first review the existing techniques in obtaining performance and energy consumption data of jobs. Then we survey the techniques for single-objective oriented predictions on runtime, queue time, power and energy consumption, cost and optimal resource configuration for input jobs, as well as multi-objective oriented predictions. We conclude after discussing future trends, research challenges and possible solutions towards intelligent resource allocation in HPC systems.     

Condition-based Maintenance with Multi-Target Classification Models

Condition-based maintenance (CBM) recommends maintenance actions based on the information collected through condition monitoring. In many modern cars, the condition of each subsystem can be monitored by onboard vehicle telematics systems. Prognostics is an important aspect in a CBM program as it deals with prediction of future faults. In this paper, we present a data mining approach to prognosis of vehicle failures. A multitarget probability estimation algorithm (M-IFN) is applied to an integrated database of sensor measurements and warranty claims with the purpose of predicting the probability and the timing of a failure in a given subsystem. The results of the multi-target algorithm are shown to be superior to a singletarget probability estimation algorithm (IFN) and reliability modeling based on Weibull analysis.     

用于高性能计算的作业调度能效性研究综述

由于科学研究与商业应用等对高性能计算的需求与日俱增,高性能计算的性能和系统规模得到迅速发展。但是,急剧增长的功耗严重限制了高性能计算系统的设计和使用,使得低功耗技术成为高性能计算领域的关键技术。作为整个系统的核心组件,作业调度系统立足有限的系统资源,对用户提交的应用进行作业-资源分配,其能效性对于整个高性能计算系统的能耗控制与调节起到至关重要的作用。首先介绍主要的能量效率技术和常用的作业调度策略,然后对当前高性能计算作业调度能效性进行分析,并讨论了其面临的挑战及未来发展方向。     

Moving from exascale to zettascale computing: challenges and techniques

High-performance computing (HPC) is essential for both traditional and emerging scientific fields, enabling scientific activities to make progress. With the development of high-performance computing, it is foreseeable that exascale computing will be put into practice around 2020. As Moore’s law approaches its limit, high-performance computing will face severe challenges when moving from exascale to zettascale, making the next 10 years after 2020 a vital period to develop key HPC techniques. In this study, we discuss the challenges of enabling zettascale computing with respect to both hardware and software. We then present a perspective of future HPC technology evolution and revolution, leading to our main recommendations in support of zettascale computing in the coming future.     

Monitoring Power Data: A first step towards a unified energy efficiency evaluation toolset for HPC data centers

The energy consumption of High Performance Computing (HPC) systems, which are the key technology for many modern computation-intensive applications, is rapidly increasing in parallel with their performance improvements. This increase leads HPC data centers to focus on three major challenges: the reduction of overall environmental impacts, which is driven by policy makers; the reduction of operating costs, which are increasing due to rising system density and electrical energy costs; and the 20 MW power consumption boundary for Exascale computing systems, which represent the next thousandfold increase in computing capability beyond the currently existing petascale systems. Energy efficiency improvements will play a major part in addressing these challenges.This paper presents a toolset, called Power Data Aggregation Monitor (PowerDAM), which collects and evaluates data from all aspects of the HPC data center (e.g. environmental information, site infrastructure, information technology systems, resource management systems, and applications). The aim of PowerDAM is not to improve the HPC data center's energy efficiency, but is to collect energy relevant data for analysis without which energy efficiency improvements would be non-trivial and incomplete. Thus, PowerDAM represents a first step towards a truly unified energy efficiency evaluation toolset needed for improving the overall energy efficiency of HPC data centers.     

Exploiting performance counters to predict and improve energy performance of HPC systems

Hardware monitoring through performance counters is available on almost all modern processors. Although these counters are originally designed for performance tuning, they have also been used for evaluating power consumption. We propose two approaches for modelling and understanding the behaviour of high performance computing (HPC) systems relying on hardware monitoring counters. We evaluate the effectiveness of our system modelling approach considering both optimizing the energy usage of HPC systems and predicting HPC applications’ energy consumption as target objectives. Although hardware monitoring counters are used for modelling the system, other methods–including partial phase recognition and cross platform energy prediction–are used for energy optimization and prediction. Experimental results for energy prediction demonstrate that we can accurately predict the peak energy consumption of an application on a target platform; whereas, results for energy optimization indicate that with no a priori knowledge of workloads sharing the platform we can save up to 24% of the overall HPC system’s energy consumption under benchmarks and real-life workloads.     

Tibidabo: Making the case for an ARM-based HPC system

It is widely accepted that future HPC systems will be limited by their power consumption. Current HPC systems are built from commodity server processors, designed over years to achieve maximum performance, with energy efficiency being an after-thought. In this paper we advocate a different approach: building HPC systems from low-power embedded and mobile technology parts, over time designed for maximum energy efficiency, which now show promise for competitive performance.We introduce the architecture of Tibidabo, the first large-scale HPC cluster built from ARM multicore chips, and a detailed performance and energy efficiency evaluation. We present the lessons learned for the design and improvement in energy efficiency of future HPC systems based on such low-power cores. Based on our experience with the prototype, we perform simulations to show that a theoretical cluster of 16-core ARM Cortex-A15 chips would increase the energy efficiency of our cluster by 8.7×, reaching an energy efficiency of 1046 MFLOPS/W.     

地震灾情和地震动力学模拟系统

中国地震局地质研究所地震灾情和地震动力学模拟系统承担着海量地震数据处理、地震动力学模拟等方面的计算任务。从系统的建设目标、系统架构、软件环境、运维和业务应用等方面进行介绍,总结了该系统在运维中遇到的问题和解决措施,提出了优化并行程序在集群系统运行性能的办法,并对下一步我所高性能计算集群系统的发展和改进方向进行分析。     

基于管家式-集约化通信运维平台的设计与实现

随着国网信息化工程建设的推进,越来越多信息系统需要上线转运行,在国网信息化水平踏上新台阶的同时,对于信息运维水平、标准化程度、规范性提出了更高的要求;因此,基于业务与运行保障为中心,研究采用跨平台的J2EE技术结合B/S模式开发环境下的自动化运维平台,设计“管家式-集约化”运维平台整体的技术架构和功能模块,集成了优秀的开源运维工具,给出了部分典型流程并分析其应用成效,最后顺利通过机理评估,该平台实现既有成功整合目前现有分散的专业运维管理子系统的“管家”功能,又改变过去以设备为对象的监控,打破过去各设备系统独立管理的模式,起到“集约”监控作用,形成统一的运维智能监控一体化平台,力图为电企信息运维服务提供高效支撑平台。     

Perspectives of China’s HPC system development: a view from the 2009 China HPC TOP100 list

The China HPC TOP100 list, an annual report of the 100 most powerful high performance computing (HPC) systems installed in mainland China, has traced the rapid growth of HPC technology in China since its first publication in 2002. This paper introduces the China HPC TOP100 list and reviews the current status of HPC systems in China in terms of system features, manufactures, and areas of application using the data reported in the most recent list, published on November 1st, 2009. We provide further analysis, prediction of future trends, and directions of the development of HPC systems in China referencing historical data accumulated through archived TOP100 lists and other publically available information. We predict that the aggregated Linpack performance of the top 100 HPC systems will reach 10 PFlops in 2011, a single system with 10 PFlops peak performance will appear between 2012 and 2013, the aggregated performance of the top 100 systems will reach 100 PFlops in 2014, and a single system with 100 PFlops peak performance will appear around 2015.