Efficient Virtual Machine Placement Strategy Based on Enhanced Genetic Approach

The background of the study is rooted in the critical importance of efficient virtual machine (VM) placement in cloud computing environments. VM placement efficiency is critical in cloud computing, especially when utilizing an improved genetic technique. This paper incorporates genetic meta-heuristic to integrate VMs into the minimal number of physical machines (PMs). In order to describe the fitness function in the proposed algorithm (GaMat and GaLin), we have also incorporated predicted usage of PMs CPU usages. The aim of the article is to demonstrate the effectiveness of genetic meta-heuristic in improving VMs placement efficiency in cloud computing environments. The study focuses on minimizing energy consumption (EC) VM migration and violations of the Service Level Agreement (SLA) by integrating VMs into the minimal number of PMs using the proposed algorithms. The algorithms’ performance is evaluated by comparing them with the best-fit power-aware decreasing (Pa) VM placement strategy, based on metrics like EC, VM migration, and SLA violations. Tests were conducted in CloudSim through detailed simulations using actual workload data. The average values of the performance metrics for 10 days of the workload are collected for the proposed VM placement approach. The proposed work reduces EC by 25%, VM migration more than 50% and SLA by 58% when compared to the power aware best fit decreasing. The results of the simulations are interpreted and analysed, which shows the effectiveness of the proposed algorithms in contrast to the best-fit Pa strategy. In conclusion, the study demonstrates the effectiveness of genetic meta-heuristic based proposed algorithms (GaMat and GaLin) in optimizing VM placement in cloud computing environments. By integrating VMs into the minimal number of PMs while considering predicted resource usage, GaMat and GaLin significantly reduce EC, VM migration, and SLA violations compared to the best-fit power-aware decreasing (Pa) strategy.