Research Article
On service provisioning under a scheduled traffic model in reconfigurable WDM optical networks
@INPROCEEDINGS{10.1109/ICBN.2005.1589596, author={Bin Wang and Tianjian Li and Xubin Luo and Yuqi Fan and Chunsheng Xin}, title={On service provisioning under a scheduled traffic model in reconfigurable WDM optical networks}, proceedings={2nd International ICST Conference on Broadband Networks}, publisher={IEEE}, proceedings_a={BROADNETS}, year={2006}, month={2}, keywords={}, doi={10.1109/ICBN.2005.1589596} }
- Bin Wang
Tianjian Li
Xubin Luo
Yuqi Fan
Chunsheng Xin
Year: 2006
On service provisioning under a scheduled traffic model in reconfigurable WDM optical networks
BROADNETS
IEEE
DOI: 10.1109/ICBN.2005.1589596
Abstract
In this paper, we propose a general scheduled traffic model, sliding scheduled traffic model. In this model, the setup time ts of a demand whose holding time is T time units is not known in advance. Rather ts is allowed to begin in a pre-specified time window [l,T] subject to the constraint that l≤ts≤r-T. We then consider two problems: (1) how to properly place a demand within its associated time window to reduce overlapping in time among a set of demands; and (2) route and assign wavelengths (RWA) to a set of demands under the proposed sliding scheduled traffic model in mesh reconfigurable WDM optical networks without wavelength conversion. In addition, we consider how to rearrange a demand by negotiating a new setup time that minimizes the demand schedule change in case that the demand is blocked. To maximize temporal resource reuse, we propose a demand time conflict reduction algorithm to solve the first problem. Two algorithms, window based RWA algorithm and traffic matrix based RWA algorithm, are then proposed for the second problem. We compare the proposed RWA algorithms against a customized tabu search scheme. Simulation results show that the proposed demand time conflict reduction algorithm can resolve well over 50% of time conflicts and the space-time RWA algorithms are effective in satisfying demand requirements and minimizing total network resources used, d.