Productive Asset Management Methodology Technique in Virtual Machines to Increase CPU Utilization

Abstract

One of the technologies with the quickest growth is cloud computing, which is utilized in many applications where customers can access virtual machines (VMs) that are offered by cloud service providers in data centers. The ideal distribution should meet both the needs of users and service providers. A2OA, an adaptive Archimedes optimization algorithm, is the suggested mechanism for allocating resources in cloud computing. The optimization problem will be solved and user tasks will be distributed using this adaptive approach. The adaptive approach will combine the Seagull Optimization Algorithm with the Archimedes Optimization Algorithm (AOA) (SOA). The update procedure in the AOA will be improved. in cooperation with the SOA. The tasks will be distributed to the user optimally based on the proposed A2OA. In MATLAB, the suggested methodology will be put into practice, and results will be examined. Performance metrics such make span, load standard deviation, load ratio, user provider satisfaction level, response time, and convergence analysis will be used to assess how well the suggested methodology performs. The suggested approach will be contrasted with the traditional approaches, including the Genetic Algorithm (GA), Particle Swarm Algorithm (PSO), and Whale Optimization Algorithm (WOA), respectively. Key Word: Virtual machines, adaptive Archimedes optimization algorithm (A2OA), Seagull Optimization Algorithm (SOA), Genetic Algorithm (GA), maximum power, load ratio