WORKFLOW SCHEDULING IN COVID 19 LOCKDOWN CRISIS USING ENERGY AWARE MAKESPAN MINIMIZATION MECHANISM IN ARTIFICIAL INTELLIGENCE CLOUD COMPUTING

Abstract

Artificial Intelligence Cloud computing provides the flexible and on-demand business environment based on the resource sharing phenomena to make the service easily available for public utility. Workflow scheduling has been one of the prominent cloud computing applications; workflow comprises repeated business activity pattern which needs to execute in accordance with sequential checklist scheduling and it requires efficient QoS such as energy consumption, task execution time. In this work we address the problem of makes pan minimization and energy consumption, We have developed an efficient workflow mechanism named EAMM (Energy Aware Makes pan Minimization) to achieve the better performance in workflow scheduling. At first EAMM mechanism designs the problem of processing delay and execution time as a joint problem and solves through the same algorithm. here we focus on minimizing the make span and energy consumption in VM scheduling this is achieved through reducing the execution time on given local processor through designed algorithm. EAMM is evaluated by considering the dataset of scientific workflow based Montage and through the comparative analysis it is observed that EAMM simply outperforms the existing model in terms of total execution time and energy consumption