By Topic

Quality of service issues and nonconvex Network Utility Maximization for inelastic services in the Internet

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Abbas, G. ; Intell. & Distrib. Syst. Lab., Liverpool Hope Univ., Liverpool, UK ; Nagar, A.K. ; Tawfik, H. ; Goulermas, J.Y.

Network utility maximization (NUM) provides an important perspective to conduct rate allocation where optimal performance, in terms of maximal aggregate bandwidth utility, is generally achieved such that each source adaptively adjusts its transmission rate. Behind most of the recent literature on NUM, common assumptions are that traffic flows are elastic and that their utility functions are strictly concave. This provides design simplicity but, in practice, limits the applicability of resulting protocols, in that severe QoS problems may be encountered when bandwidth is shared by inelastic flows. This paper investigates the problem of distributively allocating data transmission rates to multiclass services, both elastic and inelastic, and overcomes the restrictive and often unrealistic assumptions. The proposed method is based on the Lagrangian Relaxation for a dual formulation that decomposes the higher dimension NUM into a number of subproblems. We use a novel Surrogate Subgradient based stochastic method to solve the dual problem. Unlike the ordinary subgradient methods, surrogate subgradient can compute optimal prices without the need to solve all the subproblems. For the lower dimension, nonlinear and nonconvex subproblems we use a hybrid particle swarm optimization (PSO) and sequential quadratic programming (SQP) method, where the objective is to achieve fast convergence as well as accuracy. We demonstrate the efficiency of the proposed rate allocation algorithm, in terms maintaining QoS for multiclass services, and validate its scalability and accuracy for large scale flows.

Published in:

Modeling, Analysis & Simulation of Computer and Telecommunication Systems, 2009. MASCOTS '09. IEEE International Symposium on

Date of Conference:

21-23 Sept. 2009