Ordonnancement des tâches éco-énergétique dans les environnements Cloud et le Fog Computing

Abstract

This thesis investigates the scheduling of IoT tasks in Cloud and Fog Computing environ- ments, with the objective of minimizing energy consumption and execution time while ensu- ring satisfactory Quality of Service (QoS). Various scheduling approaches were analyzed and compared, including traditional methods as well as intelligent techniques based on Reinfor- cement Learning. A simulation developed using SimPy and Python was used to evaluate the performance of these algorithms under different scenarios. The results demonstrate that Rein- forcement Learning-based approaches improve the overall efficiency of the system in terms of energy consumption, makespan, and deadline compliance. In particular, the Double Deep Q-Network (DDQN) approach showed superior adaptability in distributed and heterogeneous environments.