Quality of service task scheduling algorithm for time-cost trade off scheduling problem in cloud computing environment

In cloud computing environment, managing trade-offs between time and cost when executing large-scale tasks to guarantee customers minimum running time and cost of computation is not always feasible. Metaheuristics scheduling algorithms are considered as potential solutions but however, exhibit local trapping and imbalance between its global and local search. In this study, a multi-objective task scheduling model is first developed upon which a dynamic multi-objective orthogonal Taguchi-based cat swarm optimisation (dMOOTC) task scheduling algorithm is proposed to solve the model. In the developed dMOOTC algorithm, the Taguchi orthogonal approach and Pareto-optimisation strategy are used to reduced local trapping and balances between the global and local search which possibly increases its speed of convergence. Thirty independent simulation runs were conducted on CloudSim simulator tool. The results of the simulation showed that the dMOOTC scheduling algorithm showed a remarkable performance in minimising the time and cost compared to the benchmarked algorithms.