Energy-Aware Workflow Scheduling in a Fog-Cloud Computing Environment Using Non-Dominated Sorting Genetic Algorithm

Abstract

Cloud Computing is no longer just a fad and announcement. Since the beginning of this decade and up to the present, cloud computing has changed and evolved. Workflows are employed in many different scientific disciplines to coordinate data demanding applications, but their virtualized apps, platforms, compute, and storage must be promptly provisioned, scaled, and released instantly. The vast majority of researchers agree that using scientific workflows as a paradigm to define, arrange, and communicate complex scientific analysis is helpful. Workflow scheduling aims to optimally assign and execute sequence of tasks on various virtual machine instances shared and controlled by the workflow scheduler. Numerous scheduling algorithms have been proposed to tackle difficult problems more quickly than metaheuristic ones due to the NP-complete nature of this problem and its dependence on theproblem size. Energy consumption has emerged as a major issue in the cloud computing environment, in addition to the standard quality of service(QoS) restrictions like time and cost to handle this issue. The goal of this paper is to develop the Dynamic Provisioning Based on Demand (DPBD) algorithm for workflow scheduling. While sequential task completion and task priority are design restrictions, the dual objectives of the cloud service are to minimize makespan and energy usage. Using the non-dominated sorting genetic algorithm (NSGA-II), the multi-objective optimization problem's Pareto optimal solutions are found. Our approach is validated by simulating a complex workflow application.