Auto-Adaptive Multi-Objective Scheduling for Academic HPC Grids: Simulation and Execution with OAR and SimGrid - Sebastião, Xavier P.; Cavalheiro, Gerson Geraldo H. (Anais da Escola Regional de Alto Desempenho da Região Sul (ERAD-RS); 2026: Anais da XXVI Escola Regional de Alto Desempenho da Região SulDO - 10.5753/eradrs.2026.21294, 2026)
This paper proposes an auto-adaptive scheduling framework integrating OAR, a production-grade scheduler deployed in Grid’5000, with SimGrid-based simulation to enable continuous learning and multi-objective optimization. Unlike existing approaches that treat prediction, simulation, and scheduling separately, this framework establishes a closed feedback loop where runtime data from simulated Grid’5000 environments informs policy evaluation, and validated policies are designed for deployment back into OAR via its plugin architecture. The proposed framework intents to balance makespan, energy consumption, and fairness while supporting containerized scientific workflows through simulation that models operational conditions.