Réduction de la consommation d’énergie dans le Cloud Computing.

Abstract

Cloud computing is a promising technology that facilitates the execution of applications in various fields. It provides flexible and scalable services on demand with improved quality of service. Ho- wever, this flexibility leads to high energy consumption in data centers, which are the backbone of cloud infrastructure and whose energy needs are growing rapidly. Faced with this significant en- vironmental and economic challenge, reducing the energy impact of this infrastructure is a major priority. In this final year project, we focused on reducing energy consumption in cloud computing, in par- ticular by implementing three main approaches : dynamic voltage and frequency scaling (DVFS), dynamic consolidation of virtual machines, and a combination of these two techniques. We simula- ted these techniques using the SimPy library in Python and analyzed their impact on several key indicators such as energy consumption, execution time, and execution deadline compliance. Our results show that DVFS is suitable for reducing energy consumption (up to 52%), while dyna- mic consolidation optimizes energy consumption by up to 22%, and the combined method achieves a reduction of 49%. This combined approach yielded significant performance gains, particularly for high workloads.