Quantifying Uncertainty for Preemptive Resource Provisioning in the Cloud

A4 Conference proceedings

Internal Authors/Editors

Publication Details

List of Authors: Marin Aranitasi, Benjamin Byholm, Mats Neovius
Editors: Haithem Mezni, Sabeur Aridhi, Allel Hadjali
Publication year: 2017
Publisher: IEEE
Book title: Database and Expert Systems Applications (DEXA), 2017 28th International Workshop on
Start page: 127
End page: 131
ISBN: 978-1-5386-2207-0
eISBN: 978-1-5386-1051-0
ISSN: 2378-3915


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.


Cloud uncertainty, Kernel methods, Virtual machine provisioning

Last updated on 2020-31-05 at 05:30