Aldossary, M orcid.org/0000-0001-6772-700X, Djemame, K orcid.org/0000-0001-5811-5263, Alzamil, I et al. (3 more authors) (2019) Energy-aware cost prediction and pricing of virtual machines in cloud computing environments. Future Generation Computer Systems, 93. pp. 442-459. ISSN 0167-739X
Abstract
With the increasing cost of electricity, Cloud providers consider energy consumption as one of the major cost factors to be maintained within their infrastructure. Consequently, various proactive and reactive management mechanisms are used to efficiently manage the cloud resources and reduce the energy consumption and cost. These mechanisms support energy-awareness at the level of Physical Machines (PM) as well as Virtual Machines (VM) to make corrective decisions. This paper introduces a novel Cloud system architecture that facilitates an energy aware and efficient cloud operation methodology and presents a cost prediction framework to estimate the total cost of VMs based on their resource usage and power consumption. The evaluation on a Cloud testbed show that the proposed energy-aware cost prediction framework is capable of predicting the workload, power consumption and estimating total cost of the VMs with good prediction accuracy for various Cloud application workload patterns. Furthermore, a set of energy-based pricing schemes are defined, intending to provide the necessary incentives to create an energy-efficient and economically sustainable ecosystem. Further evaluation results show that the adoption of energy-based pricing by cloud and application providers creates additional economic value to both under different market conditions.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | Crown Copyright © 2018 Published by Elsevier B.V. All rights reserved. This is an author produced version of a paper published in Future Generation Computer Systems. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Cloud computing; Energy efficiency; Cost estimation; Workload prediction; Energy prediction; Pricing schemes |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number EU - European Union 687584 |
Depositing User: | Symplectic Publications |
Date Deposited: | 15 Nov 2018 13:56 |
Last Modified: | 09 Nov 2019 01:38 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.future.2018.10.027 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:138640 |