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Energy Efficient, Resource-Aware, Prediction Based VM Provisioning Approach for Cloud Environment

Energy Efficient, Resource-Aware, Prediction Based VM Provisioning Approach for Cloud Environment

Akkrabani Bharani Pradeep Kumar, P. Venkata Nageswara Rao
Copyright: © 2020 |Volume: 11 |Issue: 3 |Pages: 20
ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781799805731|DOI: 10.4018/IJACI.2020070102
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MLA

Kumar, Akkrabani Bharani Pradeep, and P. Venkata Nageswara Rao. "Energy Efficient, Resource-Aware, Prediction Based VM Provisioning Approach for Cloud Environment." IJACI vol.11, no.3 2020: pp.22-41. http://doi.org/10.4018/IJACI.2020070102

APA

Kumar, A. B. & Rao, P. V. (2020). Energy Efficient, Resource-Aware, Prediction Based VM Provisioning Approach for Cloud Environment. International Journal of Ambient Computing and Intelligence (IJACI), 11(3), 22-41. http://doi.org/10.4018/IJACI.2020070102

Chicago

Kumar, Akkrabani Bharani Pradeep, and P. Venkata Nageswara Rao. "Energy Efficient, Resource-Aware, Prediction Based VM Provisioning Approach for Cloud Environment," International Journal of Ambient Computing and Intelligence (IJACI) 11, no.3: 22-41. http://doi.org/10.4018/IJACI.2020070102

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Abstract

Over the past few decades, computing environments have progressed from a single-user milieu to highly parallel supercomputing environments, network of workstations (NoWs) and distributed systems, to more recently popular systems like grids and clouds. Due to its great advantage of providing large computational capacity at low costs, cloud infrastructures can be employed as a very effective tool, but due to its dynamic nature and heterogeneity, cloud resources consuming enormous amount of electrical power and energy consumption control becomes a major issue in cloud datacenters. This article proposes a comprehensive prediction-based virtual machine management approach that aims to reduce energy consumption by reducing active physical servers in cloud data centers. The proposed model focuses on three key aspects of resource management namely, prediction-based delay provisioning; prediction-based migration, and resource-aware live migration. The comprehensive model minimizes energy consumption without violating the service level agreement and provides the required quality of service. The experiments to validate the efficacy of the proposed model are carried out on a simulated environment, with varying server and user applications and parameter sizes.

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