Energy-aware resource utilization based on particle swarm optimization and artificial bee colony algorithms in cloud computing

Abstract

Cloud datacenters consume enormous amounts of electrical energy that increases their operational costs. This shows the importance of investing on energy consumption techniques. Dynamic placement of virtual machines to appropriate physical nodes using metaheuristic algorithms is among the methods of reducing energy consumption. In metaheuristic algorithms, there should be a balance between both exploration and exploitation aspects so that they can find better solutions in a search space. Exploration means looking for a solution in a wider area, while exploitation is producing new solutions from existence ones. Artificial bee colony (ABC) optimization, which is a biological metaheuristic algorithm, is a sign-oriented approach. It has a strong exploration ability, but a relatively weaker exploitation power. On the other hand, particle swarm optimization (PSO) is a population-based algorithm that shows better exploitation in comparison with ABC. In this research, a scheduling framework is proposed called HSF.ABC&PSO (hybrid scheduling framework based on ABC&PSO algorithms) that uses the combination of ABC and PSO algorithms. The result of experiments shows that a 4–8% of reduction in energy consumption is obtained in the mode without migration and that 3–12% of reduction is obtained in the mode with migration. In addition, a 5–14% of reduction in the computational energy consumption is obtained in the mode without migration, and 5–28% is obtained in the mode with migration. The total execution time is decreased by up to 15% in mode without migration and is approximately decreased by 27% in mode with migration. Up to 53% throughput is obtained in the mode without migration and 67% obtained with migration. Finally, 9–23% improvement in SLA violation is evaluated as well.