Aldossary, M, Alzamil, I and Djemame, K orcid.org/0000-0001-5811-5263 (2017) Towards virtual machine energy-aware cost prediction in clouds. In: Lecture Notes in Computer Science. GECON: International Conference on the Economics of Grids, Clouds, Systems, and Services, 19-21 Sep 2017, Biarritz, France. Springer , pp. 119-131. ISBN 9783319680651
Abstract
Pricing mechanisms employed by different service providers significantly influence the role of cloud computing within the IT industry. With the increasing cost of electricity, Cloud providers consider power consumption as one of the major cost factors to be maintained within their infrastructures. Consequently, modelling a new pricing mechanism that allow Cloud providers to determine the potential cost of resource usage and power consumption has attracted the attention of many researchers. Furthermore, predicting the future cost of Cloud services can help the service providers to offer the suitable services to the customers that meet their requirements. This paper introduces an Energy-Aware Cost Prediction Framework to estimate the total cost of Virtual Machines (VMs) by considering the resource usage and power consumption. The VMs’ workload is firstly predicted based on an Autoregressive Integrated Moving Average (ARIMA) model. The power consumption is then predicted using regression models. The comparison between the predicted and actual results obtained in a real Cloud testbed shows that this framework is capable of predicting the workload, power consumption and total cost for different VMs with good prediction accuracy, e.g. with 0.06 absolute percentage error for the predicted total cost of the VM.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer International Publishing AG 2017. This is an author produced version of a paper published in Lecture Notes in Computer Science. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-68066-8_10 |
Keywords: | Cloud computing; Cost Prediction; Workload prediction; ARIMA model; Power consumption; Energy efficiency |
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) |
Depositing User: | Symplectic Publications |
Date Deposited: | 21 Nov 2017 12:24 |
Last Modified: | 21 Nov 2017 14:31 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/978-3-319-68066-8_10 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:124309 |