Consider a network in which a commodity #ows at a variable rate across the arcs in order to meet supply/demand at the nodes. The aim is to optimally control the rate of #ow such that a convex objective functional is minimized. This is an optimal control problem with a large number of states, and wit
Optimal design of Service Overlay Networks with economic and performance constraints
โ Scribed by Davide Adami; Christian Callegari; Stefano Giordano; Michele Pagano; Teresa Pepe
- Publisher
- John Wiley and Sons
- Year
- 2009
- Tongue
- English
- Weight
- 601 KB
- Volume
- 23
- Category
- Article
- ISSN
- 1074-5351
- DOI
- 10.1002/dac.1072
No coin nor oath required. For personal study only.
โฆ Synopsis
Abstract
In the last years, Service Overlay Networks (SONs) have emerged as a promising means to address some of the issues (e.g. endโtoโend QoS) affecting the current Internet and to favor the development and deployment of new valueโadded Internet services. The deployment of an SON is a capitalโintensive investment, since bandwidth with certain QoS guarantees must be purchased from the individual network domains through bilateral Service Level Agreements. Thus, minimizing the economic cost of the logical endโtoโend service delivery infrastructure is one of the key objectives for the SON provider. When a SON is aimed at endโtoโend QoS provisioning, its topology must be designed so as to also satisfy the specific requirements of QoSโsensitive applications. This paper deals with the problem of planning the SON topology in order to take into account both cost and QoS constraints. More specifically, the paper proposes a set of new algorithms for the design of an optimized SON topology, which minimizes the economic cost while simultaneously meeting bandwidth and delay constraints. A performance comparison among such algorithms is finally carried out. Copyright ยฉ 2009 John Wiley & Sons, Ltd.
๐ SIMILAR VOLUMES
## Abstract This paper introduces two timeโdomain fieldโbased optimization procedures for microwave engineering. The methods are built on the foundations of MATLAB's optimization and neural network toolboxes. The first procedure makes use of a direct connection linking MATLAB's optimization toolbox