Date of Award
2013
Publication Type
Master Thesis
Degree Name
M.Sc.
Department
Computer Science
Keywords
Applied sciences, Genetic algorithm, Memetic algorithm, Optical networks, Traffic grooming
Supervisor
Arunita Jaekel
Rights
info:eu-repo/semantics/openAccess
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Abstract
In recent years there has been a growing recognition of the need for developing energy efficient network design approaches for WDM backbone networks as well. The typical approach has been to switch off some components such as line cards and router ports during low demand periods, and has focussed on traditional static and dynamic traffic models. In this paper, we present a new approach that exploits knowledge of demand holding times to intelligently share resources among non-overlapping demands and reduce the overall power consumption of the network. We consider the fixed-window scheduled traffic model (STM), and present i) a Genetic Algorithm (GA) and ii) a Memetic Algorithm (MA) based strategy that jointly minimizes both power consumption and transceiver cost for the logical topology. Simulation results clearly demonstrate that both of the proposed algorithms outperform traditional holding time unaware (HTU) approaches; the GA leads to additional improvements even compared to the shortest path holding time aware (HTA) heuristic. However, the MA manages to achieve similar results to the GA while taking up 4 to 5 times less computational resources and time to compute.
Recommended Citation
Shaabana, Ala, "The Application of Evolutionary Algorithms for Energy Efficient Grooming of Scheduled Sub-Wavelength Traffic Demands in Optical Networks" (2013). Electronic Theses and Dissertations. 4879.
https://scholar.uwindsor.ca/etd/4879