An Energy-Efficient Hybrid Scheduling Algorithm for Task Scheduling in the Cloud Computing Environments

Abstract

The main intent of the cloud computing to provide utilities to the demands of the users that are booming day by day. To meet the requirements, existing scheduling algorithms focus on the improving the performance and neglecting the energy consumed to fulfill those demands. Hence, we propose a new Hybrid Scheduling Algorithm (HS) which is based on the Genetic Algorithm (GA) and Flower Pollination based Algorithm (FPA) for cloud environments. The proposed scheduling algorithm has surpassed in terms of performance across various parameters, i.e. completion time, resource utilization, cost of computation, and energy consumption for both cloud environments than the existing scheduling algorithms (GA and FPA). The simulation results revealed that HS has demonstrated maximum resource utilization with minimum energy consumption in less completion time for the execution of the tasks as compared to the existing scheduling algorithms in both environments. The simulation results have shown that HS has utilization of the resources, 36% better than GA and 16% better than FPA in homogeneous environment whereas in heterogenous environment, HS has performed 12% better than GA and 3.8% better than FPA. The performance of HS has an improvement of 2.6% from FPA and 6.9% from the GA for completion time in homogeneous environment whereas the completion time of the HS is reduced by 17.8% from FPA and 33.7% from GA in heterogeneous environment. For energy consumption, HS has improved 22% than FPA and 11% from GA in the homogeneous environment and HS is 4% better than FPA and 14% from GA in heterogeneous environment.

Task Scheduling in Cloud Computing Based on FPA Metaheuristic Algorithm

“…The experiment results proved that the proposed technique has the ability to reduce the total execution time, makespan and increase the resource utilization efficiently. It is noteworthy that the FPA algorithm was previously applied for this problem [18]- [20]. However, they differ from our study in the way they adapt and evaluate the generated solutions, as-well-as data-set and different parameters were used.…”

An Efficient Trust-Aware Task Scheduling Algorithm in Cloud Computing Using Firefly Optimization

“…It was implemented on the Cloudsim toolkit and compared over the existing MVO and PSO algorithms and the results revealed that EMVO shows a huge impact over the baseline algorithms by minimizing the makespan while improving the resource utilization. In [ 14 ], the task scheduling algorithm was formulated by the hybridization of the GA and FPA. It was simulated on the Cloudsim toolkit and the addressed parameters were resource utilization, the completion time, and computation cost.…”

Multi‐objective task scheduling in cloud computing

“…Compared to other methods, it was shown that this technique is able to reduce the value of makespan and give a better convergence speed. Recently, Walia et al 39 presented an energy‐efficient scheduling algorithm (HS) which relies on both FPA and GA algorithms. The aim is to distribute the resources among tasks with less energy.…”