Solving Task Scheduling Problem in Cloud Computing Environment Using Orthogonal Taguchi-Cat Algorithm

Abstract

In cloud computing datacenter, task execution delay is no longer accidental. In recent times, a number of artificial intelligence scheduling techniques are proposed and applied to reduce task execution delay. In this study, we proposed an algorithm called Orthogonal Taguchi Based-Cat Swarm Optimization (OTB-CSO) to minimize total task execution time. In our proposed algorithm Taguchi Orthogonal approach was incorporated at CSO tracing mode for best task mapping on VMs with minimum execution time. The proposed algorithm was implemented on CloudSim tool and evaluated based on makespan metric. Experimental results showed for 20VMs used, proposed OTB-CSO was able to minimize makespan of 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 Particle Swarm Optimization with Simulated Annealing (HPSO-SA) algorithms. Results obtained showed OTB-CSO is effective to optimize task scheduling and improve overall cloud computing performance with better system utilization.