Energy Efficient Virtual Machine Placement in Dynamic Cloud Milieu Using a Hybrid Metaheuristic Technique

Bivasa Ranjan Parida, Amiya Kumar Rath, Bibudhendu Pati, Chhabi Rani Panigrahi, Hitesh Mohapatra, Rajkumar Buyya


Energy consumption in cloud datacenters is an alarming issue in recent times. Although handful of researches have been conducted in this domain during virtual machine placement in cloud milieu, efficient techniques are still scarce. Hence, we have worked on a novel approach to propose a hybrid metaheuristic technique combining the salp swarm optimization and emperor penguins colony algorithm, i.e. SSEPC to place the virtual machines in the most suitable datacenters as well as servers in a cloud environment, while optimizing the energy consumption. The method we propose has been compared with certain relevant hybrid algorithms in this direction like Sine Cosine Algorithm and Salp Swarm Algorithm (SCA-SSA), Genetic Algorithm and Tabu-search Algorithm (GATA), and Order Exchange & Migration algorithm and Ant Colony System algorithm (OEMACS) to test its efficacy. It is found that proposed SSEPC is consuming 4.4%, 8.2%, and 16.6% less energy as compared to its counterparts GATA, OEMACS, and SCA-SSA respectively. 


Cloud computing; salp swarm optimization; emperor penguins colony algorithm; energy consumption; virtual machine placement

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