Quality of service (QoS) task scheduling algorithm with taguchi orthogonal approach for cloud computing environment

Abstract

The increasing violation of Service Level Agreements (SLA) cause as a result of imbalance tasks allocation across Virtual Machines (VMs) has affected consumers’ Quality of Service (QoS) expectations. Researchers in the literature have put forward several models and tried to solve the problem using Artificial Intelligence (AI) scheduling techniques. Significant improvement has been recorded with the need for an ideal solution. In this paper, a multi-objective task scheduling problem with required consumers’ QoS expectations and a scheduling model in relation to the problem is presented. A Dynamic Multi-Objective Orthogonal Taguchi Based-Cat (dMOOTC) algorithm is then proposed to solve the model. CloudSim tool is used for implementation of the proposed algorithm and evaluated with metrics of execution time, execution cost, and QoS. The performance result as compared with Standard Cat Swarm Optimization (CSO), Multi-Objective Particle Swarm Optimization (MOPSO), Enhanced Parallel CSO (EPCSO), Orthogonal Taguchi Based-Cat Algorithm (OTB-CSO) shows the proposed solution outperformed better by returning good consumers’ QoS expectation.