Matchmaking: Distributed Resource Management for High Throughput Computing
HPDC '98 Proceedings of the 7th IEEE International Symposium on High Performance Distributed Computing
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
The Globus Striped GridFTP Framework and Server
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Using grid computing based components in on demand environmental data delivery
Proceedings of the second workshop on Use of P2P, GRID and agents for the development of content networks
GPC'07 Proceedings of the 2nd international conference on Advances in grid and pervasive computing
A grid computing based virtual laboratory for environmental simulations
Euro-Par'06 Proceedings of the 12th international conference on Parallel Processing
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In this paper we describe our experiences in the field of integration of geographically distributed instruments in a grid environment based on web services provided by the Globus Toolkit 4. Our first goal is to develop a common instrument interface system, based on standard interoperable components, in order to share data acquisition resources on the grid in a secure and homogeneous way and then distribute data in a grid aware content network fashion. We rely on previous developed components as our Job Flow Scheduler Service, the Resource Broker Service and the GrADS Data Service to achieve the result of a self advertised set of atmospheric and ocean data acquisition instruments as weather stations, marine surface current high frequency radars and wind profilers. We provide an example based on our grid aware virtual laboratory for environmental modeling showing how the instrument service integrates into the system, performing on line analysis and assessments on operational weather forecast model behavior. The software components we describe in this work, leveraging on the content distribution network approach, contribute to the grand challenge in the search of grid computing killer applications in the field of applied computational environmental sciences.