VIRTUAL MACHINE CONSOLIDATION USING MODIFIED LION OPTIMIZATION ALGORITHM TO IMPROVE ENERGY EFFICIENCY IN CLOUD COMPUTING ENVIRONMENT

High energy consumption is a major concern in a cloud computing environment while meeting the service demands. Energy consumption become more prominent as data centers are increasing in number and capacity. The cloud computing environment needs to deliver high quality services to their users by effectively overcoming the power shortages. An optimization based virtual machine allocation to the physical hosts reduces the energy consumption and also delivers a better quality of services to the users. In this involved work, a modified lion optimization algorithm is proposed for virtual machine allocation to physical hosts in cloud data centers to improve energy efficiency. In the modified lion optimization algorithm, levy flight distribution is employed to randomly generate the population over the solution space and Rosenbrock function is utilized to calculate the fitness value of each lion, where the best lions (virtual machines) are selected based on the obtained fitness values. From the experimental investigation, the proposed modified lion optimization algorithm reduced the energy consumption around 20% compared to the existing algorithms krill herd algorithm, modified best fit decreasing algorithm and other genetic algorithms.