Performance analysis of limited number of wavelength converters by share per node in optical switching network |
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| Affiliation: | 1. School of Chemical Engineering, Yeungnam University, 280Daehak-ro, Gyeongsan 712-749, Republic of Korea, Republic of Korea;2. Solar Photovoltaic Laboratory, Department of Physics, Sri Venkateswasra University, Tirupati 517 502, India;1. Engineering Research Center of Advanced Lighting Technology, Ministry of Education; Institute for Electric Light Sources, Fudan University, Shanghai 200433, China;2. Department of Applied Physics, Dong Hua University, Shanghai 200051, China;1. Center for Advanced Diffusion-Wave and Photoacoustic Technologies (CADIPT), Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada M5S 3G8;2. Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada M5S 3G4;3. School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, China;1. State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin 130033, China;2. University of Chinese Academy of Sciences, Beijing 100039, China |
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| Abstract: | In this paper, we present studies of an optical switching (OS) node utilizing a limited number of WCs (wavelength converters) in order to reduce the implementation cost of an OS node. The study stems from practical observation that WCs are expensive. Consequently, each output wavelength may not necessarily have its own WC and has to share a limited pool of WCs with other output wavelengths. In order to improve the utilization of the limited number of WCs, a share per node (SPN) method is proposed for the OBS node. Subsequently, a multi-dimensional Markov chain model of SPN is presented to evaluate its performance. To reduce the complexity of the multi-dimension Markov analysis, we propose a suite of methods, called randomized states (RS) multi-plane Markov chain analysis, followed by self-constrained iteration (SCI) and eventually ending with the sliding window (SW) update method, to solve for the solution. Numerical results are presented to verify the accuracy of the analytical model. With SPN, about 50% and 80% of WCs can be saved in high load and low load scenarios respectively. |
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