Predeployment of resources in agile photonic networks

This paper shows that the main challenge in remotely configurable optical networks lies in how networks should be designed and sized for dynamic traffic in the face of uncertain forecasts, so as to minimize capital expenses, predominantly due to the need to predeploy (or overprovision) resources in...

Full description

Saved in:
Bibliographic Details
Published in:Journal of lightwave technology 2004-10, Vol.22 (10), p.2236-2244
Main Authors: Gerstel, O., Raza, H.
Format: Article
Language:English
Subjects:
All-optical networks
Applied sciences
Bandwidth
Circuit properties
Configurable
Costs
Dispatching
Dynamics
Electric, optical and optoelectronic circuits
Electronics
Exact sciences and technology
Expenses
Integrated optics. Optical fibers and wave guides
Intelligent networks
Joints
Long distance private networks
Miscellaneous
Networks
Networks and services in france and abroad
Optical and optoelectronic circuits
Optical communication
Optical design
Optical fiber networks
Optical switches
Photonics
Repeaters
Systems, networks and services of telecommunications
Telecommunication traffic
Telecommunications
Telecommunications and information theory
Teleprocessing networks. Isdn
Teletraffic
Traffic engineering
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper shows that the main challenge in remotely configurable optical networks lies in how networks should be designed and sized for dynamic traffic in the face of uncertain forecasts, so as to minimize capital expenses, predominantly due to the need to predeploy (or overprovision) resources in preparation for the next connection request. In addition, this paper demonstrates how the operational savings promised by such capabilities measure up against the added capital expenses. Consequently, new ways to provision resources in the network to minimize these expenses are proposed. The results are based on real-world long-haul networks and costs.
ISSN:0733-8724
1558-2213
DOI:10.1109/JLT.2004.833283