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VM Reservation Plan Adaptation Using Machine Learning in Cloud Computing

  • Bartlomiej Sniezynski
  • Piotr NawrockiEmail author
  • Michal Wilk
  • Marcin Jarzab
  • Krzysztof Zielinski
Open Access
Article
  • 17 Downloads

Abstract

In this paper we propose a novel reservation plan adaptation system based on machine learning. In the context of cloud auto-scaling, an important issue is the ability to define and use a resource reservation plan, which enables efficient resource scheduling. If necessary, the plan may allocate new resources upon reservation where a sufficient amount of resources is available. Our solution allows the updating of a reservation plan initially prepared by an administrator. It makes it possible to adapt reservation plans one or more weeks ahead. Hence, it allows time for the administrator to analyze the plan and discover potential problems with resource under-provisioning or over-provisioning, which may prevent server overload in the former case and unnecessary expenses in the latter. It also makes it possible to extract and analyze the knowledge learned, which may provide useful information about resource usage characteristics. The proposed solution is tested on OpenStack using real Wikipedia server traffic data. Experimental results demonstrate that machine learning enables an improvement in resource usage.

Keywords

Automated cloud resource planning Supervised machine learning Online plan adaptation 

Notes

Acknowledgements

The research presented in this paper was supported by Samsung Research Poland.

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Copyright information

© The Author(s) 2019

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Faculty of Computer Science, Electronics and Telecommunications, Department of Computer ScienceAGH University of Science and TechnologyKrakowPoland
  2. 2.Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Department of Computer ScienceAGH University of Science and TechnologyKrakowPoland
  3. 3.Samsung R&D Institute PolandSamsung ElectronicsKrakowPoland

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