Dynamic CPU resource provisioning in virtualized servers using maximum correntropy criterion Kalman filters

Evagoras Makridis, Kyriakos M. Deliparaschos, Evangelia Kalyvianaki, Themistoklis Charalambous

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

Virtualized servers have been the key for the efficient deployment of cloud applications. As the application demand increases, it is important to dynamically adjust the CPU allocation of each component in order to save resources for other applications and keep performance high, e.g., the client mean response time (mRT) should be kept below a Quality of Service (QoS) target. In this work, a new form of Kalman filter, called the Maximum Correntropy Criterion Kalman Filter (MCC-KF), has been used in order to predict, and hence, adjust the CPU allocations of each component while the RUBiS auction site workload changes randomly as the number of clients varies. MCC-KF has shown high performance when the noise is non-Gaussian, as it is the case in the CPU usage. Numerical evaluations compare our designed framework with other current state-of-the-art using real-data via the RUBiS benchmark website deployed on a prototype Xen-virtualized cluster.
Original languageEnglish
Title of host publicationProceedings of the 22nd IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2017
PublisherIEEE
Number of pages8
ISBN (Electronic)978-1-5090-6505-9
DOIs
Publication statusPublished - 8 Jan 2018
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Emerging Technologies and Factory Automation - Limassol, Cyprus
Duration: 12 Sep 201715 Sep 2017
Conference number: 22

Publication series

NameProceedings IEEE International Conference on Emerging Technologies and Factory Automation
PublisherIEEE
ISSN (Print)1946-0740
ISSN (Electronic)2379-9560

Conference

ConferenceIEEE International Conference on Emerging Technologies and Factory Automation
Abbreviated titleETFA
CountryCyprus
CityLimassol
Period12/09/201715/09/2017

Keywords

  • resource provisioning
  • virtualized servers
  • CPU
  • allocation
  • CPU usage
  • RUBiS
  • Kalman filters

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  • Cite this

    Makridis, E., Deliparaschos, K. M., Kalyvianaki, E., & Charalambous, T. (2018). Dynamic CPU resource provisioning in virtualized servers using maximum correntropy criterion Kalman filters. In Proceedings of the 22nd IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2017 (Proceedings IEEE International Conference on Emerging Technologies and Factory Automation). IEEE. https://doi.org/10.1109/ETFA.2017.8247677