Fast fully parallel thinning algorithms
CVGIP: Image Understanding
Interfacing Condor and PVM to harness the cycles of workstation clusters
Future Generation Computer Systems - Special issue: resource management in distributed systems
Adaptive Scheduling for Master-Worker Applications on the Computational Grid
GRID '00 Proceedings of the First IEEE/ACM International Workshop on Grid Computing
HiPC '00 Proceedings of the 7th International Conference on High Performance Computing
Nimrod: a tool for performing parametrised simulations using distributed workstations
HPDC '95 Proceedings of the 4th IEEE International Symposium on High Performance Distributed Computing
An Enabling Framework for Master-Worker Applications on the Computational Grid
HPDC '00 Proceedings of the 9th IEEE International Symposium on High Performance Distributed Computing
Adaptive Scheduling for Task Farming with Grid Middleware
International Journal of High Performance Computing Applications
Idle regulation in non-clairvoyant scheduling of parallel jobs
Discrete Applied Mathematics
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Strategies for scheduling parallel applications on a distributed system must trade-off processor application speed-up and resource efficiency. Most existing strategies focus mainly on achieving high application speed-up without taking into account the efficiency factor. This paper presents our experiences with a self-adaptive scheduling strategy that dynamically adjusts the number of resources used by an application based on performance measures gathered during its execution. The strategy seeks to maximize resource efficiency while minimizing the impact in loss of speedup. It also uses the measured times to decide how to assign tasks to resources. This work has been carried out in the context of opportunistic clusters of machines and we report the results achieved by our strategy when it was applied to an image thinning application run on a Condor pool.