Hybrid Teaching–Learning-Based Optimization for Workflow Scheduling in Cloud Environment

Abstract

At present, workflow scheduling in cloud computing environment is still a challenging optimization topic due to its NP-complete characteristics. In order to obtain better scheduling results, researchers are constantly coming up with new methods. In this study, we offer a hybrid metaheuristic for solving workflow scheduling in cloud to minimize the makespan of the workflow considering the heterogeneity of virtual resources. This hybrid approach combines the excellent optimization properties of Heterogeneous Earliest Finish Time (HEFT), Teaching-Learning-Based Optimization (TLBO), Opposition-Based Learning (OBL), and genetic manipulations, which is named Hybrid TLBO (HTLBO). Firstly, a HEFT-based method is proposed to produce the high-quality diverse initial population. Secondly, a Mixed OBL (MOBL) model is designed, in which the boundary search information and the population historical search information are systematically taken into account. Finally, an enhanced learner stage using genetic operations are added to effectively help the algorithm to jump out of the local optima. Rigorous experiments over various scientific workflows are conducted to validate HTLBO's performance. The obtained results are compared to HEFT and some state-of-the-art hybrid metaheuristics in terms of average makespan, running time and non-parametric statistics. A significant improvement in schedule quality demonstrates that HTLBO can increase population diversity and achieve a good balance between scheduling effectiveness and efficiency.