Optimizing energy consumption for a performance-aware cloud data center in the public sector

In general, cloud environments are based on the pay-per-use model, whereby clients pay for the information resources provided by cloud service providers. Users rent and use resources as needed while avoiding the high costs of large-scale resource acquisitions, and providers maximize their profits by managing information resources at a minimum cost while upholding service-level agreements. As the number of resources gradually increases, power supply shortages may arise. This study focuses on the fact that the CPU utilization rate of the server running in the data center is less than 30% and idle servers running only the OS consume more than half of the power consumed by hosts running with maximum CPU utilization and speed. Therefore, this study proposes an approach to enhance data center efficiency through improved management of energy consumption. We present a method to minimize energy consumption while processing the same workload, i.e., ultimately reducing the energy consumed by operating servers. Energy consumption and SLA violation rate were used as evaluation metrics of the optimization model to meet the minimum performance target.