平面有限元单元网格自动生成

本文通过对平面三角形单元、四边形单元节点信息及单元定义的分析,用C语言编制了能自动生成平面三节点三角形单元,四节点、八节点四边形单元的有限元问题的前处理程序FEMPRE2D．C．通过使用FEMPRE2D．C程序可大大节省数据录入人员的工作量,减少准备数据的时间,并能消除录入数据时可能发生的错误,从而为正确解决工程问题提供可靠的保证．     

基于UC-7420的电能量管理终端的设计与实现

在智能电网的建设中,电能量管理系统是必备的系统,电能量管理终端又是此系统的重要组成部分。本文介绍一种基于MOXA公司的UC-7420平台的电能量管理终端,UC-7420的CPU是精简指令集IntelXscale架构的IXP422处理器,预装了嵌入式Linux操作系统。用户软件采用模块化设计,各个模块由单独的线程实现,整体采用多线程技术。终端可以启用多个抄表线程,极大地提高了抄表的效率和电力数据的实时性;添加了shell脚本,提高了终端的稳定性和可靠性。     

分布式网格资源发现研究综述

网格是实现分布式资源共享的重要系统,资源发现是此类系统应提供的一项主要服务。给定用户的资源需求,系统应能发现并返回匹配的资源列表。作为网格研究的热点课题,资源发现受到了众多研究者的关注,取得了丰富的研究成果,也预留了若干问题尚未解决。详述了该领域近年来的研究成果,包括基于有结构和无结构对等模型的扁平模式、超级对等架构、分组聚类模型和语义匹配等分布式方法。对每类方案,讨论了其代表系统、核心问题及实现机制等内容。     

面向Web的网格服务互相调用研究

网格技术是一门新兴的信息技术,是Internet发展的必然结果。该文主要从网格安全基础设施GSI概况;WSRF技术实现网格资源及服务的互相调用;VO中的网格服务安全通信;服务协同工作影响因素;网格服务协同实例——Girdftp服务的交互过程等方面,着重研究在异构环境和不同主机环境下虚拟组织中的网格服务互相调用问题。     

基于蚁群算法的网格资源发现模型研究

本文通过对传统的网格资源发现存在的问题进行分析,针对其不足,引入蚁群算法,提出基于蚁群算法的网格资源发现模型（AA_GRRM）,设计并分析AA_GRRM的体系结构,并对其关键模块分析设计,以提高网格资源发现效率。     

统一数据管理空间在大规模并行作业计算中的应用

大规模并行作业的计算通常涉及海量的计算数据和众多的高性能计算设备.随着网格计算技术帮助人们进行计算的同时,大规模并行作业的数据规模的增长也越来越快,对计算速度的要求也越来越高,为了充分利用网格等计算平台上的资源,提高作业的计算效率,人们通常需要将待计算的数据进行分组,然后分别上传至不同的平台上进行计算,这对科学研究和数据管理造成了极大的不便.本文提出了一个针对大规模并行作业计算的统一数据管理空间,实现了异构网格和计算平台上数据的逻辑整合,从而大大提高了对计算数据的管理效率,加快了科学活动的进程.本文最后通过统一数据管理空间在大规模虚拟筛选中的应用,对该统一空间的数据传输效率和数据管理能力进行了分析.     

基于GAPSO混合算法的网格工作流调度研究

网格工作流调度关注大规模的资源和任务调度,是一个复杂且具有挑战性的问题,它影响着网格工作流执行成功与否以及效率的高低。提出了基于遗传粒子群(GAPSO)的混合算法,引用了特殊的适应度函数,设定了动态的交叉和变异概率,并提出了动态切换算法的方法。结合各自算法的优势,在算法运行初期利用遗传算法的全局搜索能力进行优化搜索,在后期利用粒子群较强的局部搜索能力加快收敛速度。仿真结果表明该算法在执行时间方面有一定的优越性,能更有效地解决网格工作流调度问题。     

Online algorithms for advance resource reservations

We consider the problem of providing QoS guarantees to Grid users through advance reservation of resources. Advance reservation mechanisms provide the ability to allocate resources to users based on agreed-upon QoS requirements and increase the predictability of a Grid system, yet incorporating such mechanisms into current Grid environments has proven to be a challenging task due to the resulting resource fragmentation. We use concepts from computational geometry to present a framework for tackling the resource fragmentation, and for formulating a suite of scheduling strategies. We also develop efficient implementations of the scheduling algorithms that scale to large Grids. We conduct a comprehensive performance evaluation study using simulation, and we present numerical results to demonstrate that our strategies perform well across several metrics that reflect both user- and system-specific goals. Our main contribution is a timely, practical, and efficient solution to the problem of scheduling resources in emerging on-demand computing environments.     

A general distributed scalable grid scheduler for independent tasks

We consider non-preemptively scheduling a bag of independent mixed tasks (hard, firm and soft) in computational grids. Based upon task type, we construct a novel generalized distributed scheduler (GDS) for scheduling tasks with different priorities and deadlines. GDS is scalable and does not require knowledge of the global state of the system. It is composed of several phases: a multiple attribute ranking phase, a shuffling phase, and a task-resource matched peer to peer dispatching phase. Results of exhaustive simulation demonstrate that with respect to the number of high-priority tasks meeting deadlines, GDS outperforms existing approaches by 10%–25% without degrading schedulability of other tasks. Indeed, with respect to the total number of schedulable tasks meeting deadlines, GDS is slightly better. Thus, GDS not only maximizes the number of mission-critical tasks meeting deadlines, but it does so without degrading the overall performance. The results have been further confirmed by examining each component phase of GDS. Given that fully known global information is time intensive to obtain, the performance of GDS is significant. GDS is highly scalable both in terms of processors and number of tasks—indeed it provides superior performance over existing algorithms as the number of tasks increase. Also, GDS incorporates a shuffle phase that moves hard tasks ahead improving their temporal fault tolerance. Furthermore, since GDS can handle mixed task types, it paves the way to open the grid to make it amenable for commercialization. The complexity of GDS is O(n²m)$O\left(n^{2}m\right)$ where n$n$ is the number of tasks and m$m$ the number of machines.