On Fully Decentralized Resource Discovery in Grid Environments
GRID '01 Proceedings of the Second International Workshop on Grid Computing
SCAMP: Peer-to-Peer Lightweight Membership Service for Large-Scale Group Communication
NGC '01 Proceedings of the Third International COST264 Workshop on Networked Group Communication
Matchmaking: Distributed Resource Management for High Throughput Computing
HPDC '98 Proceedings of the 7th IEEE International Symposium on High Performance Distributed Computing
A quadtree approach to domain decomposition for spatial interpolation in grid computing environments
Parallel Computing - Special issue: High performance computing with geographical data
A Self-Organized Grouping (SOG) Method for Efficient Grid Resource Discovery
GRID '05 Proceedings of the 6th IEEE/ACM International Workshop on Grid Computing
Grid computing of spatial statistics: using the TeraGrid for G i*(d) analysis
Concurrency and Computation: Practice & Experience - Grids and Geospatial Information Systems
A theoretical approach to the use of cyberinfrastructure in geographical analysis
International Journal of Geographical Information Science
Proceedings of the 2011 TeraGrid Conference: Extreme Digital Discovery
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Spatial middleware serves as a glue for high-performance and distributed GIS services to harness the computational capabilities of cyberinfrastructure. This paper focuses on the development of an important component of spatial middleware -- a distributed resource broker that matches computation tasks of GIS and spatial analysis to appropriate cyberinfrastructure resources to solve computationally intensive GIS and spatial analysis problems. This distributed resource broker is built on computational intensity estimations and a self-organized grouping (SOG) framework. Specifically, we use computational intensity information to enable cyberinfrastructure resource brokering for spatial middleware by exploiting spatial characteristics; and adapt the SOG framework to enhance resource brokering performance through the use of a space filling curve. A new overlay network is designed to inherit the good performance and distributed self-organizing nature of SOG while the use of computational intensity information enhances computational performance of resource brokering for GIS and spatial analysis applications.