Quantifying Uncertainty for Preemptive Resource Provisioning in the Cloud

Marin Aranitasi, Benjamin Byholm, Mats Neovius

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

1 Citation (Scopus)

Abstract

To satisfy quality of service requirements in a cost-efficient manner, cloud service providers would benefit from providing a means for quantifying the level of operational uncertainty within their systems. This uncertainty arises due to the dynamic nature of the cloud. Since tasks requiring various amounts of resources may enter and leave the system at any time, systems plagued by high volatility are challenging in preemptive resource provisioning. In this paper, we present a general method based on Dempster-Shafer theory that enables quantifying the level of operational uncertainty in an entire cloud system or parts thereof. In addition to the standard quality metrics, we propose monitoring of system calls to capture historical behavior of virtual machines as an input to the general method. Knowing the level of operational uncertainty enables greater accuracy in online resource provisioning by quantifying the volatility of the deployed system.

Original languageUndefined/Unknown
Title of host publicationDatabase and Expert Systems Applications (DEXA), 2017 28th International Workshop on
EditorsHaithem Mezni, Sabeur Aridhi, Allel Hadjali
PublisherIEEE
Pages127–131
ISBN (Electronic)978-1-5386-1051-0
ISBN (Print)978-1-5386-2207-0
DOIs
Publication statusPublished - 2017
MoE publication typeA4 Article in a conference publication
EventDatabase and Expert Systems Applications (DEXA) - 28th International Workshop on Database and Expert Systems Applications (DEXA)
Duration: 28 Aug 201731 Aug 2017

Conference

ConferenceDatabase and Expert Systems Applications (DEXA)
Period28/08/1731/08/17

Keywords

  • Cloud uncertainty
  • Kernel methods
  • Virtual machine provisioning

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