Grid computing is an efficient paradigm to aggregate diverse and geographically distributed computing resources and provide a transparent and services-sharing environment. The high-performance applications in grid have high demand on quality of service (QoS). In this paper, we present a comprehensive study on the QoS requirements of such high-end grid-based applications, propose a new QoS-enabled services system framework, and describe a prototype implementation of the framework that aims to provide stream media transmission services in grid. Our goal is to provide useful, transparent and convenient QoS for high-end applications in the grid environment.

This paper proposes a dynamic adaplive coschednlin model DASIC to take aklvantage of excess available resources in a network of workstations (SOW), Beaidea coachoduling related subtaska dynamically, DASIC can acalo up or down the process space depending upon the number of availoble processors on an NOW. Based on the dynamic idle processor group (TPG), DASIC employs three modules. the coscheduling module, the scalable scheduling module and the load balancing module, and uses six algorithms to achieve scalability, A simplified DASIC was also implemented. and expcrimental remls are prcsented in this paper, which show that it can maximize system utilisation, and achieve task parallelism as much as possible.

In this paper, we propose an active grid resource management system supported by active networks for computational grid. First, we construct a scalable two-level resource management architecture. In this architecture, resources in the system are divided into multiple autonomous domains. In each domain, an active resource tree (ART) is organized with resources as its leaf nodes and active routers as non-leaf nodes. Resource information is disseminated into each active router in ART, and nodes in ART work cooperatively to discover and schedule resources. Communications between domains are done via the root of ART. Second, the resource trade between consumers and providers is carried out by an improved barter marketing model. The proposed model reduces the complexity and cost of the trade, and provides desired soft quality of service as well. We illustrate that the proposed system exhibits good scalability, autonomy, flexibility, and fault-tolerance.

在一个空间数据库中，空间数据总是以最细节的信息进行储存，以满足不同应用的需求。制图学中的制图综合就是为不同的应用生成恰当的细节层面。随着空间数据库应用的日益广泛，以及越来越多的基于Internet的空间应用的出现，对空间数据的有效自动综合成为一个紧要问题。深入探讨了当前这一领域存在的问题，提出一种面向数据库的概化处理技术，利用空间索引机制z-value进行扩展，并将其应用到空间数据的自动综合技术中，通过对空间数据库的设计和对空间操作的处理来实现一个有效的空间数据库自动综合系统。

This paper presents an adaptive scalable scheduling model ASCS based on NOWs, which provides a dynamic load sharing facility by allowing tasks to scale up to utilize desired excess computing capacity depending upon available workstations on a NOW, and also scale down to maintain workstation autonomy. In this paper, a hierarchical scheduling policy is described: the inter-cluster scheduling provides a simple, fast and less-overhead load sharing among clusters in the system; and the intra-cluster scheduling provides a high performance scheduling in a cluster, and an adaptive coscheduling is implemented, some scalable algorithms are introduced to schedule those parallel/distributed applications to adapt to system changes. The 1simulation results show that the ASCS Model has better performance with less overhead, and it can well adapt to different system structures, and also well adapt to large scale systems.