CPU-RAM-based energy-efficient resource allocation in clouds

Cloud computing provides online services and solutions for dynamic and on-demand resource provisioning. These resources consume high energy leading to higher operational expenditures and carbon footprints in data centers. There are several research works performed on energy efficiency of data centers, but mostly focus on energy consumption of a single factor, i.e., CPU, leaving the RAM neglected. Recently, the researchers have focused on the impact of RAM's energy consumption on the data centers. Studies show that RAM consumes 25% of a server's overall energy. In this paper, we propose two sets of schemes that consider the server capacity for virtual machine consolidation to reduce the overall energy cost. The proposed techniques are implemented in CloudSim, and the results are compared with state-of-the-art solutions. Our proposed techniques reduce energy consumption and maintain a service level agreement to satisfy the customer requirements with a minimum cost.