A generic learning multi-agent-system approach for spatio-temporal-, thermal- and energy-aware scheduling

Christina Herzog (1) , Jean-Marc Pierson (2)

1 LIUPPA - Laboratoire Informatique de l'Université de Pau et des Pays de l'Adour
2 IRIT-SEPIA - Système d’exploitation, systèmes répartis, de l’intergiciel à l’architecture

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.

Mots clés: Multi agent system; Thermal model; Thermal aware scheduling; Makespan; Energy consumption; Energy efficiency

Domaines: Réseaux et télécommunications [cs.NI]; Système d'exploitation [cs.OS]; Architectures Matérielles [cs.AR]; Systèmes embarqués

Christina Herzog, Jean-Marc Pierson. A generic learning multi-agent-system approach for spatio-temporal-, thermal- and energy-aware scheduling. 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP 2018), Mar 2018, Cambridge, United Kingdom. pp.1-9. ⟨hal-02651524⟩