Design of Energy Aware Scheduling Algorithm for Executing Scientific Workflows in Cloud
Balamurugan S1, Saraswathi S2

1S. Balamurugan, Research Scholar in Department of CSE, Pondicherry Engineering College, Puducherry, India.
2S. Saraswathi, Professor Department of Information Technology, Pondicherry Engineering College, Pondicherry, India.
Manuscript received on November 24, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 1305-1311 | Volume-9 Issue-2, December, 2019. | Retrieval Number:  A2013109119/2020©BEIESP | DOI: 10.35940/ijeat.A2013.129219
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Abstract: The usage of cloud computing and its resources for the execution of scientific workflow is a rapidly increasing demand. The Scientific applications are generally large in scale; even a single scientific workflow includes more number of complex tasks. Execution of these tasks can be made successful only by deploying it in the cloud virtual machines, because only cloud environment can only provide very large number of computing assets. In cloud, every processing resource is given as Virtual Machine. Any scientific workflow deployed in the cloud needs large number of virtual machines so; huge amount of computational energy is spent by the virtual machines to execute multifaceted scientific workflows. Hence there arises the need to utilize the cloud resources in an energy efficient way. Also, if the virtual machines are planned to schedule in an energy efficient manner there is an increase of makepsan of the workflow which is going to be an important parameter for completing the workflow within the deadline. So, the need for executing scientific workflows in energy efficient way with reduced makespan becomes a major issue among the researchers. It also becomes very challenging task to executing a scientific workflow in within the given deadline of a task in the given workflow. To address these issues, a new Energy Aware workflow scheduling algorithm is proposed and designed with improved makespan for the execution of different scientific applications in cloud environment.
Keywords: Workflow, Scientific Application, Task Scheduling, Virtual Machines; Power Utilization, Energy Efficiency, Task assignment, Task migration, makespan, Genetic Algorithm, Fitness Function.