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A Generic Learning Multi-agent-System Approach for Spatio-Temporal-, Thermal- and Energy-Aware Scheduling

descriptionPublicationkeyboard_double_arrow_right Conference object , Article 01 Mar 2018 France Publisher:IEEEJournal:2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP)

Authors: Herzog, Christina; Pierson, Jean-Marc;

doi: 10.1109/pdp2018.2018.00010

A Generic Learning Multi-agent-System Approach for Spatio-Temporal-, Thermal- and Energy-Aware Scheduling

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Abstract

This paper proposes an agent based approach to the scheduling of jobs in data centers under thermal constraints. The model encompasses both temporal and spatial aspects of the temperature evolution using a unified model, taking into account the dynamics of heat production and dissipation. Agents coordinate to eventually move jobs to the best suitable place and to adapt dynamically the frequency settings of the nodes to the best combination. Several objectives of the agents are compared under different circumstances by an extensive set of experiments.

Country

France

Related Organizations

Paul Sabatier University
France
University of Pau and Pays de l'Adour
France
Institut National Polytechnique de Toulouse
France
University of Toulouse
France

Keywords

[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR], Thermal aware scheduling, Makespan, Système d'exploitation, Réseaux et télécommunications, Thermal model, Systèmes embarqués, Energy consumption, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], Energy efficiency, Architectures Matérielles, Multi agent system, [INFO.INFO-ES]Computer Science [cs]/Embedded Systems, [INFO.INFO-OS]Computer Science [cs]/Operating Systems [cs.OS]

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	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average