Hybrid Meta-heuristic Genetic Algorithm: Differential Evolution Algorithms for Scientific Workflow Scheduling in Heterogeneous Cloud Environment

Abstract

The gaint cabailities of cloud computing in providing online services via Internet attract the attention of the distributed sector due to its huge abilities that include storage, processing, software, databases, and servers that are shared simultaneously over the Internet by remote users geographically dispersed. Increasing the enormous amount of generating data through big data platforms and the use of IoT devices connected via the network have exploited the computational power of the cloud. However, the high utilization of the cloud leads to a longer execution time for a specific task. This paper proposing the hybrid strategy of scheduling the workflow in cloud computing called Genetic Algorithm with Differential Evolution (GA-DE). This research aims to investigate how heterogeneous cloud computing affects workflow scheduling. This study is aimed at reducing makespan and verifying if the metaheuristic technology is more suitable for the distributed environment by comparing it to existing heuristics, such as HEFT-Downward Rank,HEFT-Upward Rank,HEFT-Level Rank, and meta-heuristic algorithm GA. The proposed algorithm is validated through extensive experiments compared to three scientific workflows (Epigenomics,Cybershake,and Montage). Based on the simulation result GA-DE algorithm proves its superiority against the other comparing algorithms in term of makespan. Furthermore, the conducted experiment proves that montage scientific workflow is more proper for executing workflow scheduling in heterogeneous cloud computing.