Research Article
Solving Queueing Network Models in Cloud Provisioning Contexts
@ARTICLE{10.4108/icst.valuetools.2014.258191, author={Marta Beltran and Francisco Carriedo}, title={Solving Queueing Network Models in Cloud Provisioning Contexts}, journal={EAI Endorsed Transactions on Cloud Systems}, volume={1}, number={3}, publisher={EAI}, journal_a={CS}, year={2015}, month={2}, keywords={balanced job bounds, cloud provisioning, mean value analysis, queueing networks}, doi={10.4108/icst.valuetools.2014.258191} }- Marta Beltran
Francisco Carriedo
Year: 2015
Solving Queueing Network Models in Cloud Provisioning Contexts
CS
EAI
DOI: 10.4108/icst.valuetools.2014.258191
Abstract
In recent years the research community and most of cloud users are trying to propose flexible and general mechanisms to determine how much virtual resources need to be allocated to each tier of the applications executed on cloud infrastructures. The objective of this virtual provisioning is twofold: minimizing resources consumption and meeting the service level agreement (SLA). Most of the current cloud provisioning and scaling solutions are based on analytical models of applications, trying to automate the provisioning decisions making "what-if" response time predictions. Queueing network (QN) models have demonstrated to be a good choice in this kind of contexts. In this work we compare, performing an exhaustive set of experiments on a real cloud architecture with a new provisioning mechanism, exact solutions with approximate solutions estimated from bounding techniques in order to obtain conclusions about the most efficient way of solving these models when making cloud provisioning decisions.
Copyright © 2015 M. Beltran and F. Carriedo, licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
