网格计算及其在大型数值计算中的应用

针对在没有超级计算机的情况下,通过共享网络将不同地点的大量计算机互联,将各处计算机的多余处理器能力合在一起,形成巨大的处理能力,从而形成虚拟的超级计算机,使得"虚拟化的"、无缝的资源共享成为可能,从而支持不同组织和部门之间的协作,解决大型数值计算的问题。并以网格计算工具包Globus Toolkit为基础,在网格基本原理的指导下,运行了一个具体的网格应用实例,求解了"素性判定"、"高精度运算"等问题,验证了该方法的正确性。     

校园网格环境构建的关键技术研究

网格计算是新型分布式计算技术。网格是一个集成的计算与资源环境,它能够充分吸纳各种计算资源并将它们转化成一种方便可靠的计算能力。文中论述了网格计算的特点和体系结构及其与传统分布式计算的区别,进而分析了校园网格环境构建的关键技术。校园网格环境的构建是网格普及化必经的关键一步。在校园网格系统构建中除了文中提到的关键技术之外还有诸多问题如安全机制、资源管理、任务管理、通信技术等都有待于进一步研究和具体化。     

校园网格环境构建的关键技术研究

网格计算是新型分布式计算技术。网格是一个集成的计算与资源环境，它能够充分吸纳各种计算资源并将它们转化成一种方便可靠的计算能力。文中论述了网格计算的特点和体系结构及其与传统分布式计算的区别，进而分析了校园网格环境构建的关键技术。校园网格环境的构建是网格普及化必经的关键一步。在校园网格系统构建中除了文中提到的关键技术之外还有诸多问题如安全机制、资源管理、任务管理、通信技术等都有待于进一步研究和具体化。     

浅谈网格安全问题

在分析网格环境安全特点和需求的基础上,本文介绍了网格计算的安全问题,探讨了网格在安全方面需要考虑的特性和网格安全技术研究现状,最后给出了网格安全的发展前景。     

基于网格技术的仿真系统互联与互操作

当前国内基于HLA/RTI开发的大多数仿真系统都是在局域网内构建单联邦,实现联邦内各联邦成员之间的互联与互操作。该文将网格技术与HLA/RTI相结合,实现广域网内多个仿真系统之间的互联与互操作,完成联邦级或系统级的功能共享和重用,并对基于网格技术互联的系统体系结构及关键技术进行了介绍。     

网格Cache若干问题分析

网格中资源能力的不均衡和异构特性会给系统造成瓶颈，引起客户端延迟。网格cache系统能够有效地缓解这一问题。本文分析了网格cache的研究目的、网格cache和传统cache的关系，介绍了目前与网格cache相关的研究和网格cache系统设计的若干问题。     

网格任务调度机制的研究

由于资源具有异构、动态等特性,计算网格环境下的调度就成了一个非常复杂且具有挑战性的问题.介绍网格任务调度的研究现状,分析网格任务调度算法的基本原理.提出一种网格计算任务调度模型.     

网格计算安全模型的研究

网格计算是一种新型的计算模式,具有数据处理能力强和运行成本低的特点。近年来,有关网格计算系统的安全性设计越发受到关注,但现有的网络安全技术在处理复杂的、动态的网格计算系统时,在访问控制、权限授予等环节的效果并不理想。首先分析了网格计算系统的安全性需求,其次研究并借鉴了现有颇具代表性的网络安全架构,最后我们从网格安全设计和实现的角度,对网格计算系统提出一种新的安全体系结构模型,并深入讨论了网格计算系统中各实体间交互时所采取的相应安全策略。     

织女星网格的体系结构研究

网格技术是高性能计算机研究的热点，网格的体系结构是构建网格系统的基础，在分析比较现有网格技术的基础上，提出了织女星网格的体系结构设计，并对其中的关键问题进行了研究，织女星网格体系结构的基本思路是实现一台虚拟的、单一的网格超级计算机，其核心是Vege设计思想，织女星网格体系结构的关键组成包括网格硬件、网格互联系统以及网格操作系统，同时提出了资源路由器、网格计算协议、网格浏览器等重要概念。     

存储网格面面观

网格研究迅速成为计算机行业的热门话题,参照适用领域,一般将网格研究分为计算网格、数据网格、信息网格、知识网格等。它们解决的一个共同问题是分布的、海量的资源共享和计算,其中数据与存储虚拟化是网格计算的基础,我们也可称为存储网格或者网格存储。     

网格计算的研究与探讨

网格是一个基础体系结构,它耦合了:计算机、软件和数据库,通过局域网/广域网,形成一个统一的集成资源。网格计算(Grid Computing)指的是独立的用户组在高速网络上动态地共享计算机资源,形成对用户透明的超级计算环境,以满足不断变化的计算需求。本文介绍了网格计算结构、基本功能、网络的项目及体系结构,并对国内外著名的网格计算理论和应用作介绍,分析了目前网格计算研究的现状和发展前景。     

HITGRID:基于动态规划技术的网格任务调度中间件

随着计算规模不断扩大,人们对并行计算调度的性能要求也不断提高。网格技术的出现,解决了计算资源的共享性、异构性、规模可扩展性、鲁棒性以及安全性等方面的问题,但同时也在资源调度方面带来了新的挑战。HITGRID是一个基于Globus网格工具平台上的网格调度中间件,它接收用户提交的计算任务,针对用户对计算资源提出的要求,在网格环境中查找符合条件的资源进行调度。文中讨论了将NWS预测技术封装为标准网格资源信息的方法,以及资源调度的性能模型,给出了调度中资源选择的策略。最后通过一个计算大规模N体问题的实例表明笔者的工作是有成效的。     

多机群网格的数据负载均衡模型

在多个计算机机群构成的网格环境下,为了提高异构资源的利用率,提出了一个数据负载均衡模型。根据查询节点的计算能力、存储能力、通信能力,研究了查询节点的性能模型。利用数据饱和度、数据负载矩阵、数据迁移技术描述了系统的数据负载均衡机制。试验结果表明：该模型适合海量数据的查询。     

Grid Computing in China

Grid computing presents a new trend to distributed computation and Internet applications, which can construct a virtual single image of heterogeneous resources, provide uniform application interface and integrate widespread computational resources into super, ubiquitous and transparent aggregation. In the adoption of Grid computing, China, who is facing more resource heterogeneity and other specific demands, has put much effort on both research and practical utilization. In this paper, we introduce the major China Grid research projects and their perspective applications. First we give the overview of the four government-sponsored programs in Grid, namely the China National Grid, ChinaGrid, NSFC Grid, and ShanghaiGrid. Then we present six representative ongoing Grid systems in details, which are categorized into Grid middleware and Grid application. This paper provides the general picture of Grid computing in China, and shows the great efforts, devotion and confidence in China to use Grid technology to boost the society, economics and scientific research.     

使用GPDK实现网格门户的开发

开放网格服务体系结构(OGA)是基于网格和Web服务领域的概念和技术。门户是访问高性能计算机系统上的服务的用户界面。使用GPDK(Grid Portal Development Kit)可以方便地构造Web界面。本文给出了一个模拟复杂流体运动的实验网格系统。在该系统中，门户是在GPDK平台上设计的。     

Vega: A Computer Systems Approach to Grid Computing

In this paper, we contrast four approaches for Grid computing, and discuss a computer systems approach in detail. This approach views a Grid as a distributed computer system, and its main concerns are systems abstractions and constructs, such as the Grid equivalents of computer architecture, address space, process, device, file system, user/developers interface. Following this methodology, we identified several essential issues, developed a Vega Grid systems architecture, and proposed several systems techniques such as Grid routers, Grid address spaces, Grid process (grip), Grid community (agora), and a Grid Service Markup Language (GSML) software suite. We evaluated the computer systems approach and the associated techniques by discussing an OSGA-compliant Grid system software implementation and a travel agency example.     

基于资源树的网格资源遍历并行实现

本文提出了网格环境下的并行计算模型G-PRAM,并在此基础上提出网格资源树遍历的一种并行实现算法,最后给出示例和说明。     

Integrating Computing Resources on Multiple Grid-Enabled Job Scheduling Systems Through a Grid RPC System

We present a framework for a parallel programming model by remote procedure calls, which bridge large-scale computing resource pools managed by multiple Grid-enabled job scheduling systems. With this system, the user can exploit not only remote servers and clusters, but also the computing resources provided by Grid-enabled job scheduling systems located on different sites. This framework requires a Grid remote procedure call (RPC) system to decouple the computation in a remote node from the Grid RPC mechanism and uses document-based communication rather than connection-based communication. We implemented the proposed framework as an extension of the OmniRPC system, which is a Grid RPC system for parallel programming. We designed a general interface to easily adapt the OmniRPC system to various Grid-enabled job scheduling systems, including XtremWeb, CyberGRIP, Condor and Grid Engine. We show the preliminary performance of these implementations using a phylogenetic application. We found that the proposed system can achieve approximately the same performance as OmniRPC and can handle interruptions in worker programs on remote nodes. Yoshihiro Nakajima is a Research Fellow of the Japan Society for the Promotion of Science     

A novel approach to resource scheduling for parallel query processing on computational grids

Advances in network technologies and the emergence of Grid computing have both increased the need and provided the infrastructure for computation and data intensive applications to run over collections of heterogeneous and autonomous nodes. In the context of database query processing, existing parallelisation techniques cannot operate well in Grid environments because the way they select machines and allocate tasks compromises partitioned parallelism. The main contribution of this paper is the proposal of a low-complexity, practical resource selection and scheduling algorithm that enables queries to employ partitioned parallelism, in order to achieve better performance in a Grid setting. The evaluation results show that the scheduler proposed outperforms current techniques without sacrificing the efficiency of resource utilisation. Recommended by: Ioannis Vlahavas     

Ontology‐based Grid resource management

Grid resources are typically diverse in nature with respect to their software and hardware configurations, resource usage policies and the kind of application they support. Aggregating and monitoring these resources, and discovering suitable resources for the applications become a challenging issue. This is partially due to the representation of Grid metadata supported by the existing Grid middleware which offers limited scope for matching the job requirements that directly affect scheduling decisions. This paper proposes a semantic component in conventional Grid architecture to support ontology‐based representation of Grid metadata and facilitate context‐based information retrieval that complements Grid schedulers for effective resource management. Web Ontology language is used for creating Grid resource ontology and Algernon inference engine has been used for resource discovery. This semantic component has been integrated with conventional Grid schedulers. Several experiments have also been carried out to investigate the performance overhead that arises while integrating this component with Grid schedulers. Copyright © 2009 John Wiley & Sons, Ltd.