We deal with the following scheduling problem: an infinite number of tasks must be scheduled for processing on a finite number of heterogeneous machines, such as all tasks are sent to execution with a minimum delay. The tasks have causal dependencies and are generated in the context of biomedical applications, and produce results relevant for the medical domain, such as diagnosis support or drug dose adjust measures. The proposed scheduling model had a starting point in two known bounded number of processors algorithms: Modified Critical Path and Highest Level First With Estimated Times. Several steps were added to the original implementation along with a merge stage in order to combine the results obtained for each of the previously scheduled tasks. Regarding the implementation, a simulator was used to analyze and design the scheduling algorithms.

Adapting MCP and HLFET Algorithms to Multiple Simultaneous Scheduling

Iacono, Mauro
;
2017

Abstract

We deal with the following scheduling problem: an infinite number of tasks must be scheduled for processing on a finite number of heterogeneous machines, such as all tasks are sent to execution with a minimum delay. The tasks have causal dependencies and are generated in the context of biomedical applications, and produce results relevant for the medical domain, such as diagnosis support or drug dose adjust measures. The proposed scheduling model had a starting point in two known bounded number of processors algorithms: Modified Critical Path and Highest Level First With Estimated Times. Several steps were added to the original implementation along with a merge stage in order to combine the results obtained for each of the previously scheduled tasks. Regarding the implementation, a simulator was used to analyze and design the scheduling algorithms.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11591/385737
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