Name
Abstract | ||
|---|---|---|
Optical networks have attracted much more attention in the last decades due to its huge bandwidth (Tbps). The Wavelength Division Multiplexing (WDM) is a technology that aims to make the most of this networks by dividing each single fiber link into several wavelengths of light or channels. Each channel operates in the range of Gbps; unfortunately, the requirements of the vast majority of current traffic connection requests are a few Mbps, causing a waste of bandwidth at each channel. We can solve this drawback by equipping each optical node with an access station for multiplexing or grooming several low-speed requests onto one single high-speed channel. This problem of grooming low-speed requests is known in the literature as the Traffic Grooming problem. In this work, we formulate the Traffic Grooming problem as a Multiobjective Optimization Problem, optimizing simultaneously the total throughput, the number of transceivers used, and the average propagation delay. We propose the use of the Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D). The experiments are conducted on three optical network topologies and diverse scenarios. The results report that the MOEA/D algorithm works more efficiently than other multiobjective approaches and other single-objective heuristics published in the literature. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2463372.2463443 | GECCO |
Keywords | Field | DocType |
wdm optical network,optical node,low-speed request,optical network,multiobjective evolutionary algorithm,single fiber link,huge bandwidth,optical network topology,multiobjective optimization problem,single high-speed channel,d algorithm,traffic grooming,multiobjective optimization | Wavelength-division multiplexing,Propagation delay,Computer science,Computer network,Communication channel,Network topology,Bandwidth (signal processing),Throughput,Multiplexing,Traffic grooming,Distributed computing | Conference |
Citations | PageRank | References |
1 | 0.35 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alvaro Rubio-Largo | 1 | 98 | 13.00 |
| Qingfu Zhang | 2 | 7634 | 255.05 |
| Miguel A. Vega-Rodriguez | 3 | 145 | 18.81 |