Advances in Grid and Pervasive Computing
Description
This book constitutes the refereed proceedings of the Second International Conference on Grid and Pervasive Computing, GPC 2007, held in Paris, France in May 2007.
The 56 revised full papers and 12 revised short papers were carefully selected from 217 submissions during two rounds of reviewing and improvement. The papers address all aspects of grid and pervasive computing and focus on topics such as cluster computing, high performance computing, grid computing, semantic Web and semantic grid, service-oriented computing, peer-to-peer computing, pervasive computing, mobile computing, network storage, as well as grid and pervasive related applications.
Written for: Researchers and professionals
Keywords: QoS, Web services, ad-hoc networks, biometrics, cluster computing, data grid, distributed algorithms, distributed computing, distributed processing, distributed systems, fault tolerance, fuzzy logic, grid computing, grid scheduling, high performance computing, interoperability, load balancing, middleware, mobile systems, multicast protocols, network computing, parallel processing, peer-to-peer systems, performance modeling, pervasive computing, reliability, routing, semantic Web, sensor networks, ubiquitous computing, user interfaces, wireless networking.
More details
Other editions
Additional editions
Content
A High-Performance Virtual Storage System for Taiwan UniGrid (p. 43)
1 Introduction
With the rapid growth of computing power and storage capacity of computers, many researchers and scientists have been concentrated on the development of various Grid systems to efficiently utilize distributed computing and storage resources in recent years.
In Taiwan, a community of educational and research organizations interested in Grid computing technologies founded a Grid computing platform, called Taiwan UniGrid [1]. These organizations contribute their resources of computer clusters for sharing and collaboration. The objective of Taiwan UniGrid is to provide educational and research organizations with a powerful computing platform where they can study Grid-related issues, practice parallel programming on Grid environments and execute computing/data-intensive applications.
As similar to other Grid systems, Taiwan UniGrid consists of three primary portions: Computational Grid, Data Grid and Web Portal. Computational Grid is responsible for managing scattered and heterogeneous computing resources and scheduling the jobs submitted by users. Data Grid is a virtual storage infrastructure that integrates distributed, independently managed data resources and allows users to save and retrieve their data without understanding the configuration of underlying storage resources.
Web Portal, developed by National Tsing Hua University, is a uniform user interface by which Grid users can design workflow, submit jobs, manage data, monitor job and resource status, etc. In this paper, we will present the development of the data management system for Taiwan UniGrid. As the distribution of storage resources and the growth of data size, the needs for efficient Grid data management are continuously increasing.
In these years, many research and scientific organizations have engaged in building data management and storage tools for Grids, such as SDSC SRB (Storage Resource Broker) [2], SciDAC Data Grid Middleware [3], GriPhyN Virtual Data System [4], etc. SRB is a general Data Grid middleware that integrates distributed and heterogeneous storage resources and provides virtualized access interface. It has been a production data management tool and adopted by several Grid projects.
Thus, among these tools, we decide to build our virtual storage system for Taiwan UniGrid based on SRB, while developing additional features that are not well supported by SRB. Before implementing the virtual storage system, we elicited requirements from the user and manager needs. Herein, in additional to the basic Data Grid functions provided by SRB, we identify three main requirements of the current development listed as follows.
• High-performance data transfer:
Since the size of data generated by scientific instruments and Grid applications has grown into the range of Terabytes, large data transfer over the Internet usually leads to a long latency and becomes a bottleneck for job executions. Thus, the need for high-performance data transfer is an important issue in Taiwan UniGrid.
• Data sharing:
Two important concepts of Grids are sharing and collaboration. Grid users, such as scientists and researchers, are accustomed to retrieve data collected by remote scientific instruments, analyze these retrieved data via various analysis tools, and share the analyzed results for further processing. Therefore, how to facilitate Grid users to contribute or get shared data with ease is a crucial requirement in the development of a data management system.
