eprintid: 2614
rev_number: 6
eprint_status: archive
userid: 6
dir: disk0/00/00/26/14
datestamp: 2015-02-23 09:38:59
lastmod: 2015-05-19 09:17:36
status_changed: 2015-02-23 09:38:59
type: article
metadata_visibility: show
creators_name: Cello, Marco
creators_name: Gnecco, Giorgio
creators_name: Marchese, Mario
creators_name: Sanguineti, Marcello
creators_id: 
creators_id: giorgio.gnecco@imtlucca.it
creators_id: 
creators_id: 
title: Narrowing the Search for Optimal Call-Admission Policies Via a Nonlinear Stochastic Knapsack Model
ispublished: pub
subjects: QA75
divisions: CSA
full_text_status: none
keywords: Stochastic knapsack, Nonlinear constraints, Call admission control, Coordinate-convex policies, Structural properties
note: Published online: 30 April 2014
abstract: Call admission control with two classes of users is investigated via a nonlinear stochastic knapsack model. The feasibility region represents the subset of the call space, where given constraints on the quality of service have to be satisfied. Admissible strategies are searched for within the class of coordinate-convex policies. Structural properties that the optimal policies belonging to such a class have to satisfy are derived. They are exploited to narrow the search for the optimal solution to the nonlinear stochastic knapsack problem that models call admission control. To illustrate the role played by these properties, the numbers of coordinate-convex policies by which they are satisfied are estimated. A graph-based algorithm to generate all such policies is presented.
date: 2015-03
date_type: published
publication: Journal of Optimization Theory and Applications
volume: 164
number: 3
publisher: Springer-Verlag
pagerange: 819-841
id_number: 10.1007/s10957-014-0570-2
refereed: TRUE
issn: 0022-3239
official_url: http://link.springer.com/article/10.1007%2Fs10957-014-0570-2
citation:   Cello, Marco and Gnecco, Giorgio and Marchese, Mario and Sanguineti, Marcello  Narrowing the Search for Optimal Call-Admission Policies Via a Nonlinear Stochastic Knapsack Model.  Journal of Optimization Theory and Applications, 164 (3).  pp. 819-841.  ISSN 0022-3239      (2015)