Orthogonal Taguchi-based cat algorithm for solving task scheduling problem in cloud computing

In cloud computing datacenter, task execution delay is a common phenomenal cause by task imbalance across virtual machines (VMs). In recent times, a number of artificial intelligence scheduling techniques are applied to reduced task execution delay. These techniques have contributed toward the need for an ideal solution. The objective of this study is to optimize task scheduling based on proposed orthogonal Taguchi-based cat swarm optimization (OTB-CSO) in order to reduce total task execution delay. In our proposed algorithm, Taguchi orthogonal approach was incorporated into tracing mode of CSO to scheduled tasks on VMs with minimum execution time. CloudSim tool was used to implement the proposed algorithm where the impact of the algorithm was checked with 5, 10 and 20 VMs besides input tasks and evaluated based on makespan and degree of imbalance metrics. Experimental results showed that for 20 VMs used, our proposed OTB-CSO was able to minimize makespan of the total tasks scheduled across VMs with 42.86, 34.57 and 2.58% improvement over minimum and maximum job first (Min-Max), particle swarm optimization with linear descending inertia weight (PSO-LDIW) and hybrid PSO with simulated annealing (HPSO-SA) and likewise returned degree of imbalance with 70.03, 62.83 and 35.68% improvement over existing algorithms. Results obtained showed that OTB-CSO is effective to optimize task scheduling and improve overall cloud computing performance through minimizing task execution delay while ensuring better system utilization.