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In cloud computing, virtual machine (VM) consolidation and migration are significant schemes to enhance the energy efficiency and costs of the cloud environment. The process of VM consolidation is to execute a more number of tasks with less power consumption. However, due to unreliable physical resources, energy consumption is increased. So, to solve this issue, improved beetle swarm optimization (IBSO) algorithm based on energy-aware VM consolidation is presented in this article. IBSO algorithm joins together beetle swarm optimization (BSO) and particle swarm optimization (PSO). The proposed algorithm is defined with the effective representation of solution and multi-objective functions such as power consumption, resource wastage, and SLA violation. Results of the work are depicted that the proposed approach outperformed different previous methods in terms of different evaluation measures.

The main objective of the proposed methodology is multi-objective job scheduling using hybridization of whale and BAT optimization algorithm (WBAT) which is used to change existing solution and to adopt a new good solution based on the objective function. The scheduling function in the proposed job scheduling strategy first creates a set of jobs and cloud node to generate the population by assigning jobs to cloud node randomly and evaluate the fitness function which minimizes the makespan and maximizes the quality of jobs. Second, the function uses iterations to regenerate populations based on WBAT behavior to produce the best job schedule that gives minimum makespan and good quality of jobs. The experimental results show that the performance of the proposed methods is better than the other methods of job scheduling problems.

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