Cloud scalable multi-objective task scheduling algorithm for cloud computing using cat swarm optimization and simulated annealing

In cloud computing, customers-desired Quality of Service (QoS) expectations are quite superficial due to lack of scalable task scheduling solutions that can adjust to long-time changes. Researchers in the literature have put forward several task scheduling algorithms to account for customers' QoS expectations. Unfortunately, most of these algorithms need improvements to ensure the provisioning of better consumers' QoS expectation. In this study, a Multi-Objective QoS model to address customers profit based on execution time and execution cost criteria is presented. A Cloud Scalable Multi-Objective Cat Swarm Optimization (CSO) based Simulated Annealing (SA) (CSM-CSOSA) algorithm is then proposed to solve the model. In this method, the Taguchi Orthogonal approach is used to enhanced the SA and incorporated into the local search of the proposed algorithm for enhancing it exploration capability. Implementation of the algorithm is carried out on CloudSim tool and evaluated using one dataset (Normal distributed) and one Parallel Workload (High-Performance Computing Center North(HPC2N)). Quantitative analysis of the algorithm performance is taken based on metrics of execution time, execution cost, QoS and percentage improvement. Result obtained is compared with that of Multi-Objective Genetic Algorithm (MOGA), Multi-Objective Ant Colony (MOSACO) and Multi-Objective Particle Swarm Optimization (MOPSO), where proposed method is able to returned substantial performance with improved QoS.