Abstract
It is very complex and of low efficiency for grid users to access heterogeneous diverse data resources distributed over the whole wide area network. Data Grid has denoted a network of storage resources, from archival systems, to caches, to databases, that are linked across a distributed network. And it should provide integrated, scalable data services which implement more wide range of transparent access to data resources in Data Grid, such as location transparency, time transparency. In this paper, we describe a SDS system which can implement integrated, scalable data services. We have implemented one of the important building blocks for SDS is the server called DSB which can provide integrated data services. The DSB bases on cluster and agent technologies. Agent-based DSB can cleverly prefetch required data, replicate them among DSBs. Each DSB based on cluster implements a single entry and a virtual integrated storage system in a data domain. As far as whole SDS, Multiple DSBs are formed cluster data services, which are scalable, and provide a single entry for all data grid users, and provide a virtual integrated mass storage system, and hide distributed heterogeneous low-level data resources, and insure load balance of each server. SDS architecture supporting these various scenarios, are also described.
Highlights
An increasing number of applications in domains such as genomics/proteomics, astrophysics, geophysics, computational neuroscience, or volume rendering, need to archive, retrieve, and process increasing large datasets(Keith Bell, Andrew Chien, Mario Lauria)
We describe a SDS system which can implement integrated, scalable data services
We have described SDS and emphasized Data Services Broker (DSB) to improve its performance
Summary
An increasing number of applications in domains such as genomics/proteomics, astrophysics, geophysics, computational neuroscience, or volume rendering, need to archive, retrieve, and process increasing large datasets(Keith Bell, Andrew Chien, Mario Lauria). These data-intensive applications are prime candidates for Data Grid as they involve remote access and extensive computation to many data repositories. Mario Lauria), so in this paper, focusing on the server, SDS adopt some approaches to implement more wide range of transparent data services in which the following strategies are used to address the purpose:.
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