Scheduling and dispatching are critical enabling technologies in supercomputing and grid computing. In these contexts, scalability is an issue: we have to allocate and schedule up to tens of thousands of tasks on tens of thousands of resources. This problem scale is out of reach for complete and centralized scheduling approaches. We propose a distributed allocation and scheduling paradigm called DARDIS that is lightweight, scalable and fully customizable in many domains. In DARDIS each task offloads to the available resources the computation of a probability index associated with each possible start time for the given task on the specific resource. The task then selects the proper resource and start time on the basis of the above probability.

DARDIS: Distributed and randomized DIspatching and scheduling

BRIDI, THOMAS;LOMBARDI, MICHELE;BARTOLINI, ANDREA;BENINI, LUCA;MILANO, MICHELA
2016

Abstract

Scheduling and dispatching are critical enabling technologies in supercomputing and grid computing. In these contexts, scalability is an issue: we have to allocate and schedule up to tens of thousands of tasks on tens of thousands of resources. This problem scale is out of reach for complete and centralized scheduling approaches. We propose a distributed allocation and scheduling paradigm called DARDIS that is lightweight, scalable and fully customizable in many domains. In DARDIS each task offloads to the available resources the computation of a probability index associated with each possible start time for the given task on the specific resource. The task then selects the proper resource and start time on the basis of the above probability.
Frontiers in Artificial Intelligence and Applications
1598
1599
Bridi, Thomas; Lombardi, Michele; Bartolini, Andrea; Benini, Luca; Milano, Michela
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/585363
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