Energy-efficient enhanced Particle Swarm Optimization for virtual machine consolidation in cloud environment

Abstract

Cloud computing is quickly used to run services of information technology by remarkable solutions for multiple welfare such as automatically improves management of resources and new service delivery system. Cloud computing supplier have to deal with reducing energy usage, to meet Service Level Agreement (SLA) demand. To decrease the cost of pay as you go technique of cloud services, resource reservation based facility is provided by cloud owners that allow users to personalize their Virtual Machines (VMs) with given time and physical resource. However, owing to Energy efficiency of Physical Machines (PMs) and efficient management of reserved services are not guaranteed. In this methodology, green cloud computing provides energy efficient data centers for the aim of cost savings, decrease negative impacts on the environment and reduces usage of energy. For better alternative for energy minimization is by exploring an alternative for energy consumption that has potential using the Particle Swarm Optimization (PSO). The PSO must be enhanced for solving the optimization problem due to more energy usage. The Enhanced PSO (E-PSO) is proposed in the research that redefines the operators and parameters of the PSO thereby adapts the energy aware local fitness that designs the coding scheme. The proposed EPSO shows an optimal VM replacement scheme that will be found with the energy consumption at the lowest. The proposed EPSO shows better energy consumption of 22% of Energy consumption was lowered better when compared with the existing methods.