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Scheduling with a processing time oracle

Authors :
Fanny Dufossé
Christoph Dürr
Noël Nadal
Denis Trystram
Óscar C. Vásquez
Data Aware Large Scale Computing (DATAMOVE )
Inria Grenoble - Rhône-Alpes
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG)
Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )
Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )
Université Grenoble Alpes (UGA)
Recherche Opérationnelle (RO)
LIP6
Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Ecole Normale Supérieure Paris-Saclay (ENS Paris Saclay)
Universidad de Santiago de Chile [Santiago] (USACH)
ANR-18-CE25-0008,Energumen,Optimiser l'énergie des plates-formes de calcul à large échelle(2018)
ANR-19-CE48-0016,AlgoriDAM,Théorie algorithmique de nouveaux modèles de données(2019)
ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)
Source :
Applied Mathematical Modelling, Applied Mathematical Modelling, 2022, 104, pp.701-720. ⟨10.1016/j.apm.2021.12.020⟩
Publication Year :
2022
Publisher :
Elsevier BV, 2022.

Abstract

International audience; In this paper we study a single machine scheduling problem with the objective of minimizing the sum of completion times. Each of the given jobs is either short or long. However the processing times are initially hidden to the algorithm, but can be tested. This is done by executing a processing time oracle, which reveals the processing time of a given job. Each test occupies a time unit in the schedule, therefore the algorithm must decide for which jobs it will call the processing time oracle. The objective value of the resulting schedule is compared with the objective value of an optimal schedule, which is computed using full information. The resulting competitive ratio measures the price of hidden processing times, and the goal is to design an algorithm with minimal competitive ratio. Two models are studied in this paper. In the non-adaptive model, the algorithm needs to decide beforehand which jobs to test, and which jobs to execute untested. However in the adaptive model, the algorithm can make these decisions adaptively depending on the outcomes of the job tests. In both models we provide optimal polynomial time algorithms following a two-phase strategy, which consist of a first phase where jobs are tested, and a second phase where jobs are executed obliviously. Experiments give strong evidence that optimal algorithms have this structure. Proving this property is left as an open problem

Details

ISSN :
0307904X
Volume :
104
Database :
OpenAIRE
Journal :
Applied Mathematical Modelling
Accession number :
edsair.doi.dedup.....f66d064c54501a5243da58f61e210c25