Multi-objective Task Scheduling to Minimize Energy Consumption and Makespan of Cloud Computing Using NSGA-II

The utilization of cloud services has significantly increased due to the easiness in accessibility, better performance, and decrease in the high initial cost. In general, cloud users anticipate completing their tasks without any delay, whereas cloud providers yearn for reducing the energy cost, which is one of the major costs in the cloud service environment. However, reducing energy consumption increases the makespan and leads to customer dissatisfaction. So, it is essential to obtain a set of non-domination solutions for these multiple and conflicting objectives (makespan and energy consumption). In order to control the energy consumption efficaciously, the Dynamic Voltage Frequency Scaling system is incorporated in the optimization procedure and a set of non-domination solutions are obtained using Non-dominated Sorting Genetic Algorithm (NSGA-II). Further, the Artificial Neural Network (ANN), which is one of the most successful machine learning algorithms, is used to predict the virtual machines based on the characteristics of tasks and features of the resources. The optimum solutions obtained using the optimization process with the support of ANN and without the support of ANN are presented and discussed.