Future Generation Computer Systems - Special issue on metacomputing
Performance Modeling and Prediction of Nondedicated Network Computing
IEEE Transactions on Computers
Adaptive Computing on the Grid Using AppLeS
IEEE Transactions on Parallel and Distributed Systems
Grid Harvest Service: A System for Long-Term, Application-Level Task Scheduling
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
A Runtime System for Autonomic Rescheduling of MPI Programs
ICPP '04 Proceedings of the 2004 International Conference on Parallel Processing
Memory Conscious Task Partition and Scheduling in Grid Environments
GRID '04 Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing
A Neural Network Based Predictive Mechanism for Available Bandwidth
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Papers - Volume 01
The GrADS Project: Software Support for High-Level Grid Application Development
International Journal of High Performance Computing Applications
Self-adjustment of resource allocation for grid applications
Computer Networks: The International Journal of Computer and Telecommunications Networking
Three-layer control policy for grid resource management
Journal of Network and Computer Applications
A survey of self-adaptive grids
IEEE Communications Magazine
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Conventional performance evaluation mechanisms focus on dedicated distributed systems. Grid computing infrastructure, on another hand, is a shared collaborative environment constructed on autonomic virtual organizations. The non-dedicated characteristic of Grid computing prevents the leverage of conventional task scheduling systems. In this study, we present the design and development of the Grid Harvest Service (GHS) performance evaluation and task scheduling system for solving large-scale applications in a shared network environment. GHS combines stochastic models and artificial intelligence learning mechanisms with task scheduling algorithms. It considers both computing and network contention and supports scheduling for single task, parallel processing, and meta-tasks. Experimental results show that GHS provides a satisfactory solution for performance prediction and task scheduling and has a real potential.