Aldossary, M and Djemame, K orcid.org/0000-0001-5811-5263 (2018) Performance and Energy-Based Cost Prediction of Virtual Machines Auto-Scaling in Clouds. In: Proceedings of the 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA 2018). 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA 2018), 29-31 Aug 2018, Prague, Czech Republic. IEEE , pp. 502-509. ISBN 978-1-5386-7383-6
Abstract
Virtual Machines (VMs) auto-scaling is an important technique to provision additional resource capacity in a Cloud environment. It allows the VMs to dynamically increase or decrease the amount of resources as needed in order to meet Quality of Service (QoS) requirements. However, the auto-scaling mechanism can be time-consuming to initiate (e.g. in the order of a minute), which is unacceptable for VMs that need to scale up/out during the computation, besides additional costs due to the increase of the energy overhead. This paper introduces a Performance and Energy-based Cost Prediction Framework to estimate the total cost of VMs auto-scaling by considering the resource usage and power consumption, while maintaining the expected level of performance. A series of experiments conducted on a Cloud testbed show that this framework is capable of predicting the auto-scaling workload, power consumption and total cost for heterogeneous VMs, with a cost-saving of up to 25% for the predicted total cost of VM self-configuration as compared to the current approaches in literature.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 IEEE. This is an author produced version of a paper published in Proceedings of the 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA 2018). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Cloud Computing; Cost Prediction; Workload Prediction; Auto Scaling; 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) |
Funding Information: | Funder Grant number EU - European Union 687584 |
Depositing User: | Symplectic Publications |
Date Deposited: | 17 Jul 2018 13:14 |
Last Modified: | 18 Jan 2019 11:28 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/SEAA.2018.00086 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:133235 |