A hybrid multi-faceted task scheduling algorithm for cloud computing environment

Cloud computing has now become the most effective platform for providing elastic and on-demand provisioning of high performance heterogeneous and homogeneous computing services basis on pay-per-use in the field high performance computing world. Task scheduling in cloud computing devotes researchers' attention to provide the optimal solution to this NP-Complete problem. An optimized task scheduling algorithm optimizes the cloud system's performance and generates the maximum profit for the cloud service provider. To overcome this issue in cloud computing, Authors developed a hybrid multi-faceted task scheduling algorithm in this research work. The proposed algorithm exploited the features of standard particle swarm optimization (PSO) and Ant Colony Optimization (ACO) technique. The PSO technique provides the best global optimal solution, whereas ACO offers the best local solution. To validate the results of the developed algorithm, performed a comparison of the makespan, cost, and resource utilization rate parameters against the well-known exiting four algorithms for the computer-generated tasks set in the cloud environment through a simulation experiment. The comparison results showed that the proposed algorithm reduces the makespan time and computation cost as well as increases resource utilization rate.