Bi-objective decision support system for task-scheduling based on genetic algorithm in cloud computing

We address in this paper the task-scheduling in cloud computing. This problem is known to be NP\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal {NP}}$$\end{document}-hard due to its combinatorial aspect. The main role of our model is to estimate the time needed to run a set of tasks in cloud and in turn reduces the processing cost. We propose a genetic approach for modelling and optimizing a task-scheduling problem in cloud computing. The experimental results demonstrate that our solution successfully competes with previous task-scheduling algorithms. For this, we develop a decision support system based on the core of CloudSim. In terms of processing cost, the obtained results show that our approach outperforms previous scheduling methods by a significant margin. In terms of makespan, the obtained schedules are also shorter than those of other algorithms.