Cost-effective Heuristic Workflow Scheduling Algorithm in Cloud Under Deadline Constraint

Abstract

Background: Cloud computing is used to deliver IT resources over the internet. Due to the popularity of cloud computing, nowadays, most of the scientific workflows are shifted towards this environment. Many algorithms have been proposed in the literature to schedule scientific workflows in the cloud, but their execution cost is very high as they are not meeting the user-defined deadline constraint. Aim: This paper focuses on satisfying the user-defined deadline of a scientific workflow while minimizing the total execution cost. Methods: So, to achieve this, we proposed a Cost-Effective under Deadline (CEuD) constraint workflow scheduling algorithm. Results: The proposed CEuD algorithm considers all the essential features of the Cloud and resolves the major issues such as performance variation and acquisition delay. Conclusion: We compared the proposed CEuD algorithm with the existing literature algorithms for scientific workflows (i.e., Montage, Epigenomics, and CyberShake) and obtained better results for minimizing the overall execution cost of the workflow while satisfying the user-defined deadline.