Résumé
As energy demands in cloud computing surge, efficient task scheduling and carbon-free energy use in mini data centers is essential. Solving scheduling problems with mixed-integer linear programming optimization methods is computationally intensive and less scalable. This paper proposes a two-step approach combining a greedy algorithm with linear programming to optimize virtual machines scheduling and energy management. We show via simulations that our method preserves solution quality, while accelerating the calculation process by 99.6 %. While the literature method requires 6 hours to solve a reference system design, our solution handles 18 times larger designs in 20 minutes only, demonstrating its scalability.