Prediction based task scheduling in distributed computing
Proceedings of the fourteenth annual ACM symposium on Principles of distributed computing
Exploiting process lifetime distributions for dynamic load balancing
Proceedings of the 1996 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
The utility of exploiting idle workstations for parallel computation
SIGMETRICS '97 Proceedings of the 1997 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
The grid
Profiling Workstations' Available Capacity for Remote Execution
Performance '87 Proceedings of the 12th IFIP WG 7.3 International Symposium on Computer Performance Modelling, Measurement and Evaluation
Matchmaking: Distributed Resource Management for High Throughput Computing
HPDC '98 Proceedings of the 7th IEEE International Symposium on High Performance 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
Design and Evaluation of a Resource Selection Framework for Grid Applications
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
Finding Idle Work Periods on Networks of Workstation
Finding Idle Work Periods on Networks of Workstation
TCON'95 Proceedings of the USENIX 1995 Technical Conference Proceedings
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Grid computing has a great potential for grand challenge scientific problems such as Molecular Simulation, High Energy Physics and Genome Informatics. Exploiting under-utilized resources is crucial for a cost-effective, large-scale grid computing platform (i.e., computational grid), but there has been little research work on how to predict what resources will be underloaded in the near future. In this paper, we analyze idle CPU cycles of PCs at university computer labs and present techniques for predicting idle cycles to be efficiently scheduled for parallel/distributed computing. Our experiments with eight month monitoring data show that the accuracy of our prediction techniques is over 85%. Especially, the ratio of critical failure, which predicts that what is actually busy be idle, was only 3.2% out of total subject PCs during the experimental period.