An Energy-Aware Approach to Virtual Machine Consolidation Using Classification and the Dragonfly Algorithm in Cloud Data Centers

Nowadays, reducing energy consumption in cloud computing is of great interest due to the high operational costs and its impact on climate change. The consolidation solution is an effective method for minimizing the number of physical machines (PMs) and reducing energy consumption. The virtual machine consolidation process encounters the challenge of reducing energy consumption while effectively managing resource allocation. The aim of this paper is to address these challenges through the classification of PMs and the use of the dragonfly algorithm. The quartile parameter is utilized to classify PMs into three categories: underloaded, medium load, and overloaded. First, we identified the overloaded PMs in the overloaded category. Then, we presented a solution to select virtual machines from an overloaded PM based on resource usage. Additionally, the Dragonfly algorithm is utilized to select destinations for hosting migrant virtual machines in the medium load category. Furthermore, we identified underloaded PMs in the underloaded categories using this algorithm. The proposed solution is evaluated using the CloudSim toolkit and tested with workloads consisting of over a thousand data points from virtual machines based on PlanetLab data. The results from the simulation experiments indicate that the proposed solution, while avoiding SLA violations and minimizing additional migrations, has significantly reduced energy consumption.