Future Generation Computer Systems - Special issue on metacomputing
Dynamic mapping of a class of independent tasks onto heterogeneous computing systems
Journal of Parallel and Distributed Computing - Special issue on software support for distributed computing
Distributed Job Scheduling on Computational Grids Using Multiple Simultaneous Requests
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
Adaptive Resource Selection for Grid-Enabled Network Services
NCA '03 Proceedings of the Second IEEE International Symposium on Network Computing and Applications
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
An Adaptive Generalized Scheduler for Grid Applications
HPCS '05 Proceedings of the 19th International Symposium on High Performance Computing Systems and Applications
Quality of Service on the Grid Via Metascheduling with Resource Co-Scheduling and Co-Reservation
International Journal of High Performance Computing Applications
Exploiting replication and data reuse to efficiently schedule data-intensive applications on grids
JSSPP'04 Proceedings of the 10th international conference on Job Scheduling Strategies for Parallel Processing
Hi-index | 0.00 |
The specific problem that underlies in collaborating Grids is scheduling of resources with no knowledge about availability of the resources due to the distributed and autonomous nature of the underlying Grid systems. In this paper, we propose a fully decentralized and probabilistic resource management scheme for Grid systems collaborating based on peer-to-peer communication paradigm. The key idea we employ is to use benchmarked performance measures about the static resource information and calculate the job execution workload. Then this benchmarked job execution time is used to predict the job scheduling feasibility in the face of resource dynamism on the target system. We design our scheme as self adjusting to the actual resource behavior and performance. Simulation results validate the appropriateness of our scheme.