现代远程教育在我国的发展分析

现代化远程教育体系是建立在现代远程教育网络之上的,而利用现代各种技术、媒体和网络等资源来建立起来的现代远程教育网络是要把教与学有效地结合起来,把各种教育资源有效地利用起来。实现全国乃至全世界教育资源的共享,实现对学生个别化终身教育,最终实现我国全民教育终身化。     

基于网格的远程教育模型的研究

介绍了网格的概念、网格计算的体系结构、基于网格的远程教育模型主要组成部分。给出了基于网格的教育模型示意图及典型的结构图,提出了基于网格的远程教育系统的核心是教育资源的共享和教学服务系统的建立。     

基于XML和J2EE的远程教育资源共享模型研究及实现

随着远程教育的不断发展，如何进行教育资源建设，实现教育资源的共享，已成为远程教育中迫切需要解决的重要课题。对远程教育和计算机中新兴的XML和J2EE技术进行了研究，从面向对象的角度出发构建了一个基于XML和J2EE的远程教育资源共享模型，并对该模型的设计及实现过程进行了分析。     

远程教育资源的描述、组织和管理系统设计

远程教育资源的描述、组织和管理系统是远程教育工程的核心，其中教育资源描述系统提出了教育资源的制定标准，这直接影响到资源库的质量，以及资源库的分布式管理与利用；教育资源组织系统主要实现基于知识点的资源组织构建功能，提供交互式的报文信息交换平台，能够实现异构应用系统以标准的信息格式在不同的教育资源库之间进行资源交流和通信；教育资源管理系统实现对各地按标准建立的教育资源库进行分布式管理和资源调度管理。     

网格环境中用于资源分配的经济模型研究

由于网格是实现对地理上广泛分布的大量异构资源进行共享，所以传统的资源调度和分配方法已经不再适用。此外为了吸引各类资源消费者和资源提供者加入网格，必须引入基于价格的经济模型，由价格浮动来反映资源供需情况的动态变化，通过供需均衡实现资源优化分配。该文讨论了现有的用于资源分配的各种经济模型以及各自的优缺点，最后总结了判断经济模型效能的各种经济和计算标准。     

基于量子遗传算法的校园网格作业调度

互联网的异构性导致了网络资源不能充分共享,传统的校园网结构使得教育资源难以大范围共享,网格技术能较好地解决这些问题。通过对校园网现状和网格技术的分析,该文提出校园网格作业调度模型,设计并实现了基于量子遗传算法的作业调度方法。算法借鉴量子比特的叠加性,采用量子编码来表征染色体,能够表示许多可能的线性叠加状态,其整体性能优于普通遗传算法。     

网格环境下教育资源元数据模型①

网格是实现教育资源共享、管理和提供信息服务的热点技术。针对现存教育资源存在的信息孤岛现象,分析教育资源的元数据功能和标准,对资源描述方法进行了比较,提出了一个基于LOM的RDF模型,并应用网格资源描述框架RDF对LOM进行了形式化描述,为教育信息的共享提供了进一步研究的思路和指导。     

网格资源管理中的经济学原理运用

网格的目标是实现对地理上广泛分布的大量异构资源进行共享。由于网格具有的异构性、分布性和动态性,传统的资源管理方法在网格环境中并不适用。此外,要吸引各类资源消费者和提供者加入网格,就必须满足他们各自的需求。将经济学原理,例如边际效用、Nash均衡等引入网格资源管理之中,能够很好地解决以上问题。该文简略地列举了近10年来在分布式系统上使用经济学模型进行资源分配的相关研究,并且对目前开展得比较好的几个网格经济学研究项目进行了分析。     

基于网格技术的WebGIS的研究

结合当前的网格技术,借鉴OGSA（Open Grid Services Architecture）体系结构,给出了一个基于网格技术的WebGIS体系结构模型,该模型包括资源层、网格中间件、服务层和应用层,详细论述了四层的结构和功能,并对GML共享技术、网格中间件和网格服务等关键技术进行了分析。实现了分布异构环境下不同格式空间信息的互操作,有利于WebGIS产品的多元化集成。     

一个网格经济学模型和信用机制

网格的目标是实现对地理上广泛分布的大量异构资源进行共享.然而,由于网格具有的异构性、分布性和动态性,网格环境中的资源管理是个复杂的问题.将经济学原理,例如一些经济学模型和市场机制引入网格之中构建网格经济学资源管理模型,能够很好地配合网格的离散特性.该文将经济学原理引入网格环境之中,提出了一个“信用机制”,使用户能够根据服务提供者的“信用”来优化选择,同时使网格更为高效和有序.     

基于经济模型的资源代理

网格计算是为解决大规模资源密集型问题而提出的新一代计算平台，是当前并行和分布处理技术的一个发展方向，而资源管理是计算网格的关键技术之一。对各种各样可利用资源的整合和管理是网格应用的基础，而资源的分布性、动态性、异构性、自治性和需要协调一致性使得网格资源的管理调度成为一个棘手的问题。目前基于市场的经济资源管理和调度算法非常适合计算网格中的资源管理问题，但有调度价格不能更改、负载平衡等问题。文中提出了“网格环境下基于经济模型的资源代理”，依靠多维QoS指导的调度策略和经济模型的启发式调节资源价格，改进和优化计算网格资源的分配。     

Collaborative data visualization for Earth Sciences with the OptIPuter

Collaborative visualization of large-scale datasets across geographically distributed sites is becoming increasingly important for Earth Sciences. Not only does it enhance our understanding of the geological systems, but also enables near-real-time scientific data acquisition and exploration across distant locations. While such a collaborative environment is feasible with advanced optical networks and resource sharing in the form of Grid, many technical challenges remain: (1) on-demand discovery, selection and configuration of supporting end and network resources; (2) construction of applications on heterogeneous, distributed environments; and (3) use of novel exotic transport protocols to achieve high performance. To address these issues, we describe the multi-layered OptIPuter middleware technologies, including simple resource abstractions, dynamic network provisioning, and novel data transport services. In this paper, we present an evaluation of the first integrated prototype of the OptIPuter system software recently demonstrated at iGrid 2005, which successfully supports real-time collaborative visualizations of 3D multi-gigabyte earth science datasets.     

基于虚拟组织的网格文件资源共享模型

网格计算主要关注大规模的资源共享,且这种共享是高度可控的。为解决网格环境下文件资源共享与管理的问题,提出了一个网格文件资源共享模型FsvGrid。该模型引入注册通知机制,并采用确定性算法与非确定性算法相结合的消息传递机制,使得网格中的各个节点之间能够高效协作;采用分层结构,屏蔽了文件资源的多样性;增加了共享的安全性,可以对共享进行控制;提出了一种依靠虚拟组织来对文件资源进行管理的方式,解决分布式资源难以管理的问题。     

面向多自治域网格的信息服务模型及其实现

网格是实现分布异构资源共享的有效模式,而信息服务实现系统服务与资源的有效管理,是网格系统的重要组成部分.ChinaGrid是由多个自治域组成的大规模网格,现有的信息服务不能满足此类系统特性与应用需求.文中提出网格信息服务体系GISA2.0,强化了域自治管理和资源信息的安全性.GISA2.0实现了可扩展的网格信息模型和面向服务、支持多种监控信息聚集的层次化信息管理框架.提出了基于分布XPath引擎的多域资源信息检索机制,实现了安全、快速和用户相关的虚拟全局资源视图.     

Dynamic workload balancing of parallel applications with user-level scheduling on the Grid

This paper suggests a hybrid resource management approach for efficient parallel distributed computing on the Grid. It operates on both application and system levels, combining user-level job scheduling with dynamic workload balancing algorithm that automatically adapts a parallel application to the heterogeneous resources, based on the actual resource parameters and estimated requirements of the application. The hybrid environment and the algorithm for automated load balancing are described, the influence of resource heterogeneity level is measured, and the speedup achieved with this technique is demonstrated for different types of applications and resources.     

基于WSRF的网络化制造资源Web服务封装的研究

提出了一种遵循WSRF技术规范的网络化制造资源封装机制,将制造资源封装成一个符合WS-Resource结构的网格服务,通过Web服务发布系统发布,屏蔽了资源的复杂性和异构性,有效地实现了制造资源的全局搜索和合理调度,使分布在不同地域、组织和系统的制造资源能够协同工作。最后,以设备资源为例验证了该技术方案的可行性和实用性。     

An improved load-balancing mechanism based on deadline failure recovery on GridSim

Grid computing has emerged a new field, distinguished from conventional distributed computing. It focuses on large-scale resource sharing, innovative applications and in some cases, high performance orientation. The Grid serves as a comprehensive and complete system for organizations by which the maximum utilization of resources is achieved. The load balancing is a process which involves the resource management and an effective load distribution among the resources. Therefore, it is considered to be very important in Grid systems. For a Grid, a dynamic, distributed load balancing scheme provides deadline control for tasks. Due to the condition of deadline failure, developing, deploying, and executing long running applications over the grid remains a challenge. So, deadline failure recovery is an essential factor for Grid computing. In this paper, we propose a dynamic distributed load-balancing technique called “Enhanced GridSim with Load balancing based on Deadline Failure Recovery” (EGDFR) for computational Grids with heterogeneous resources. The proposed algorithm EGDFR is an improved version of the existing EGDC in which we perform load balancing by providing a scheduling system which includes the mechanism of recovery from deadline failure of the Gridlets. Extensive simulation experiments are conducted to quantify the performance of the proposed load-balancing strategy on the GridSim platform. Experiments have shown that the proposed system can considerably improve Grid performance in terms of total execution time, percentage gain in execution time, average response time, resubmitted time and throughput. The proposed load-balancing technique gives 7 % better performance than EGDC in case of constant number of resources, whereas in case of constant number of Gridlets, it gives 11 % better performance than EGDC.     

Grid和P2P混合环境中一种基于信任的资源搜索机制

Grid和P2P两种分布式计算模式中的资源搜索算法均假设节点提供可靠的资源，但Grid和P2P混合计算环境的动态、异构、自组织等特点使得一些节点存在冒名和提供虚假服务等行为。本文对基于经验和最好邻居搜索机制进行改进，引入信任因子，提出了基于信任的资源搜索机制。该机制有效抑制了欺骗行为，提高了资源搜索的可靠性和安全性。     

A probabilistic and adaptive scheduling algorithm using system-generated predictions for inter-grid resource sharing

Rapid advancement and more readily availability of Grid technologies have encouraged many businesses and researchers to establish Virtual Organizations (VO) and make use of their available desktop resources to solve computing intensive problems. These VOs, however, work as disjointed and independent communities with no resource sharing between them. We, in previous work, have proposed a fully decentralized and reconfigurable Inter-Grid framework for resource sharing among such distributed and autonomous Grid systems (Rao et al. in ICCSA, [2006]). The specific problem that underlies in such a collaborating Grids system is scheduling of resources as there is very little knowledge about availability of the resources due to the distributed and autonomous nature of the underlying Grid entities. In this paper, we propose a probabilistic and adaptive scheduling algorithm using system-generated predictions for Inter-Grid resource sharing keeping collaborating Grid systems autonomous and independent. We first use system-generated job runtime estimates without actually submitting jobs to the target Grid system. Then this job execution estimate is used to predict the job scheduling feasibility on the target system. Furthermore, our proposed algorithm adapted itself to the actual resource behavior and performance. Simulation results are presented to discuss the correctness and accuracy of our proposed algorithm.

A reputation-driven scheduler for autonomic and sustainable resource sharing in Grid computing

The obstacle for the Grid to be prevalent is the difficulty in using, configuring and maintaining it, which needs excessive IT knowledge, workload, and human intervention. At the same time, inter-operation amongst Grids is on track. To be the core of Grid systems, the resource management must be autonomic and inter-operational to be sustainable for future Grid computing. For this purpose, we introduce HOURS, a reputation-driven economic framework for Grid resource management. HOURS is designed to tackle the difficulty of automatic rescheduling, self-protection, incentives, heterogeneous resource sharing, reservation, and SLA in Grid computing. In this paper, we focus on designing a reputation-based resource scheduler, and use emulation to test its performance with real job traces and node failure traces. To describe the HOURS framework completely, a preliminary multiple-currency-based economic model is also introduced in this paper, with which future extension and improvement can be easily integrated into the framework. The results demonstrate that our scheduler can reduce the job failure rate significantly, and the average number of job resubmissions, which is the most important metric in this paper that affects the system performance and resource utilization from the perspective of users, can be reduced from 3.82 to 0.70 compared to simple sequence resource selection.