A Stochastic Approach for Scheduling AI Training Jobs in GPU-based Systems

In this work, we optimize the scheduling of Deep Learning (DL) training jobs from the perspective of a Cloud Service Provider running a data center, which efficiently selects resources for the execution of each job to minimize the average energy consumption while satisfying time constraints. To mode...

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Vydané v:	IEEE transactions on cloud computing Ročník 12; číslo 1; s. 1 - 17
Hlavní autori:	Filippini, Federica, Anselmi, Jonatha, Ardagna, Danilo, Gaujal, Bruno
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.01.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Average energy consumption minimization Cloud computing Computer Science Constraint modelling Costs Deep Learning Energy consumption Energy costs GPU cluster GPU sharing Graphics processing units Job Tardiness Linear programming Mixed integer Nonlinear programming Optimal scheduling Optimization Scheduling Stochastic processes Training Job Tardiness consumption minimization GPU sharing Scheduling GPU cluster Deep Learning Average energy
ISSN:	2168-7161, 2372-0018
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