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Power-Aware Mechanism for Scheduling Scientific Workflows in Cloud Environment

Power-Aware Mechanism for Scheduling Scientific Workflows in Cloud Environment

Kirankumar V. Kataraki, Sumana Maradithaya
Copyright: © 2021 |Volume: 12 |Issue: 1 |Pages: 17
ISSN: 1947-8186|EISSN: 1947-8194|EISBN13: 9781799861508|DOI: 10.4018/IJISMD.2021010102
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MLA

Kataraki, Kirankumar V., and Sumana Maradithaya. "Power-Aware Mechanism for Scheduling Scientific Workflows in Cloud Environment." IJISMD vol.12, no.1 2021: pp.22-38. http://doi.org/10.4018/IJISMD.2021010102

APA

Kataraki, K. V. & Maradithaya, S. (2021). Power-Aware Mechanism for Scheduling Scientific Workflows in Cloud Environment. International Journal of Information System Modeling and Design (IJISMD), 12(1), 22-38. http://doi.org/10.4018/IJISMD.2021010102

Chicago

Kataraki, Kirankumar V., and Sumana Maradithaya. "Power-Aware Mechanism for Scheduling Scientific Workflows in Cloud Environment," International Journal of Information System Modeling and Design (IJISMD) 12, no.1: 22-38. http://doi.org/10.4018/IJISMD.2021010102

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Abstract

Cloud computing is a platform that hosts various services and applications for users and businesses to access computing as a service. Cloud provider offers two distinct types of plans: reserved service and on-demand service. Cloud resources need to be allocated efficiently, and task needs to be scheduled efficiently such that the performance can be enhanced. In this research work, the authors have proposed a novel mechanism named PAMP (performance aware mechanism for parallel computation) for scheduling scientific workflows. At first, the resources are allocated using the optimal resource allocation mechanism. Then tasks are scheduled in parallel utilizing the task scheduling algorithm. Further, they consider energy and time as constrained to makespan optimization. The evaluation is carried out by considering the scientific workflows cyber snake with its different variant, and the comparative analysis is carried out by varying the number of virtual machines. The proposed methodology outperforms the existing model.

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