A Lagrangean relaxation-based approach for routing and wavelength assignment in multigranularity optical WDM networks

被引:14
|
作者
Lee, SSW [1 ]
Yuang, MC
Tien, PL
Lin, SH
机构
[1] ITRI, Opt Commun & Networking Technol Dept, Comp & Commun Res Labs, Hsinchu 310, Taiwan
[2] Natl Chiao Tung Univ, Dept Comp Sci & Informat Engn, Hsinchu 30050, Taiwan
关键词
combinatorial optimization problem; Lagrangean relaxation; multigranularity switching capabilities; routing and wavelength assignment (RWA); wavelength-division multiplexing (WDM);
D O I
10.1109/JSAC.2004.835523
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Optical wavelength-division multiplexed (WDM) networks often include optical cross-connects with multigranularity switching capability such as switching on a single lambda, a waveband, or an entire fiber basis. In addition, it has been shown that routing and wavelength assignment (RWA) in an arbitrary mesh WDM network is an NP-complete problem. In this paper, we propose an efficient approximation approach, called Lagrangean relaxation with heuristics (LRH), aimed to resolve RWA in multigranularity WDM networks particularly with lambda and fiber switches. The task is first formulated as a combinatorial optimization problem in which the bottleneck link utilization is to be minimized. The LRH approach performs constraint relaxation and derives a lower-bound solution index according to a set of Lagrangean multipliers generated through subgradient-based iterations. In parallel, using the generated Lagrangean multipliers, the LRH approach employs a new heuristic algorithm to arrive at a near-optimal upper-bound solution. With lower and upper bounds, we conduct a performance study on LRH with respect to accuracy and convergence speed under different parameter settings. We further draw comparisons between LRH and an existing practical approach via experiments over randomly generated and several well-known large sized networks. Numerical results demonstrate that LRH outperforms the existing approach in both accuracy and computational time complexity, particularly for larger sized networks.
引用
收藏
页码:1741 / 1751
页数:11
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