Open Access

REVIEW

A Review of Energy-Related Cost Issues and Prediction Models in Cloud Computing Environments

Mohammad Aldossary^*

Department of Computer Science, College of Arts and Science, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia

* Corresponding Author: Mohammad Aldossary. Email:

Computer Systems Science and Engineering 2021, 36(2), 353-368. https://doi.org/10.32604/csse.2021.014974

Received 30 October 2021; Accepted 28 November 2020; Issue published 05 January 2021

Abstract

With the expansion of cloud computing, optimizing the energy efficiency and cost of the cloud paradigm is considered significantly important, since it directly affects providers’ revenue and customers’ payment. Thus, providing prediction information of the cloud services can be very beneficial for the service providers, as they need to carefully predict their business growths and efficiently manage their resources. To optimize the use of cloud services, predictive mechanisms can be applied to improve resource utilization and reduce energy-related costs. However, such mechanisms need to be provided with energy awareness not only at the level of the Physical Machine (PM) but also at the level of the Virtual Machine (VM) in order to make improved cost decisions. Therefore, this paper presents a comprehensive literature review on the subject of energy-related cost issues and prediction models in cloud computing environments, along with an overall discussion of the closely related works. The outcomes of this research can be used and incorporated by predictive resource management techniques to make improved cost decisions assisted with energy awareness and leverage cloud resources efficiently.

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Keywords

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