eprintid: 2604
rev_number: 6
eprint_status: archive
userid: 6
dir: disk0/00/00/26/04
datestamp: 2015-02-11 14:24:13
lastmod: 2015-02-11 14:24:13
status_changed: 2015-02-11 14:24:13
type: article
metadata_visibility: show
creators_name: Casale, Giuliano
creators_name: Tribastone, Mirco
creators_id: 
creators_id: mirco.tribastone@imtlucca.it
title: Modelling exogenous variability in cloud deployments
ispublished: pub
subjects: QA75
divisions: CSA
full_text_status: none
abstract: Describing exogenous variability in the resources used by a cloud application leads to stochastic performance models that are difficult to solve. In this paper, we describe the blending algorithm, a novel approximation for queueing network models immersed in a random environment. Random environments are Markov chain-based descriptions of timevarying operational conditions that evolve independently of the system state, therefore they are natural descriptors for exogenous variability in a cloud deployment. The algorithm adopts the principle of solving a separate transient-analysis subproblem for each state of the random environment. Each subproblem is then approximated by a system of ordinary differential equations formulated according to a fluid limit theorem, making the approach scalable and computationally inexpensive. A validation study on several hundred models shows that blending can save up to two orders of magnitude of computational time compared to simulation, enabling efficient exploration of a decision space, which is useful in particular at design-time.
date: 2013-03
date_type: published
publication: Performance Evaluation Review
volume: 40
number: 4
publisher: ACM
pagerange: 73-82
id_number: doi:10.1145/2479942.2479951
refereed: TRUE
issn: 0163-5999
official_url: http://dx.doi.org/10.1145/2479942.2479951
citation:   Casale, Giuliano and Tribastone, Mirco  Modelling exogenous variability in cloud deployments.  Performance Evaluation Review, 40 (4).  pp. 73-82.  ISSN 0163-5999      (2013)