Cognitive algorithm using fuzzy reasoning for software-defined optical network

We propose a cognitive algorithm based on Fuzzy C-Means (FCM) technique for the learning and decision-making functionalities of software-defined optical networks (SDONs). SDON is a new optical network paradigm where the control plane is decoupled from the data plane, thus providing a degree of software programmability to the network. Our proposal is to add the FCM algorithm to the SDON control plane in order to achieve a better network performance, when compared with a non-cognitive control plane. In this context, we illustrate the use of the FCM algorithm for determining, in real time and autonomously, the modulation format of high-speed flexible rate transponders in accordance with the quality of transmission of optical channels. The performance of this FCM algorithm is evaluated via computational simulations for a long-haul network and compared to the case-based reasoning (CBR) algorithm, which is commonly used in optical cognitive networks. We demonstrate that FCM outperforms CBR in both fastness and error avoidance, achieving 100 % of successful classifications, being two orders of magnitude faster. Additionally, we propose a definition of cognitive optical networking and an architecture for the SDON control plane including the FCM engine.

[1]  Bing Liu,et al.  A Kernel Clustering-Based Possibilistic Fuzzy Extreme Learning Machine for Class Imbalance Learning , 2015, Cognitive Computation.

[2]  Carolina Fortuna,et al.  Trends in the development of communication networks: Cognitive networks , 2009, Comput. Networks.

[3]  Miquel Garrich,et al.  SDN-enabled EDFA gain adjustment cognitive methodology for dynamic optical networks , 2015, 2015 European Conference on Optical Communication (ECOC).

[4]  Hazem Shatila.  Adaptive Radio Resource Management in Cognitive Radio Communications using Fuzzy Reasoning , 2012 .

[5]  Yuefeng Ji,et al.  Performance evaluation of multi-stratum resources integration based on network function virtualization in software defined elastic data center optical interconnect. , 2015, Optics express.

[6]  Nabil Bitar,et al.  Extending software defined network principles to include optical transport , 2013, IEEE Communications Magazine.

[7]  R.J. Almeida,et al.  Comparison of fuzzy clustering algorithms for classification , 2006, 2006 International Symposium on Evolving Fuzzy Systems.

[8]  Ioannis Tomkos,et al.  DICONET NPOT: An Impairments Aware Tool for Planning and Managing Dynamic Optical Networks , 2011, Journal of Network and Systems Management.

[9]  Ting Wang,et al.  Terabit/s optical superchannel with flexible modulation format for dynamic distance/route transmission , 2012, OFC/NFOEC.

[10]  Piet Demeester,et al.  Optical networking: past, present and future , 2000 .

[11]  Noboru Yoshikane,et al.  Scalable software-defined optical networking with high-performance routing and wavelength assignment algorithms. , 2015, Optics express.

[12]  Ioannis Tomkos,et al.  A Cognitive Quality of Transmission Estimator for Core Optical Networks , 2012, Journal of Lightwave Technology.

[13]  Ting Wang,et al.  Virtual infrastructure embedding over software-defined flex-grid optical networks , 2013, 2013 IEEE Globecom Workshops (GC Wkshps).

[14]  Nick McKeown,et al.  OpenFlow: enabling innovation in campus networks , 2008, CCRV.

[15]  N. Merayo,et al.  Case-Based Reasoning (CBR) to estimate the Q-factor in optical networks: An initial approach , 2011, 2011 16th European Conference on Networks and Optical Communications.

[16]  Biswanath Mukherjee,et al.  Software-defined optical networks (SDONs): a survey , 2014, Photonic Network Communications.

[17]  Baoping Tang,et al.  Life grade recognition of rotating machinery based on Supervised Orthogonal Linear Local Tangent Space Alignment and Optimal Supervised Fuzzy C-Means Clustering , 2015 .

[18]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[19]  Ting Wang,et al.  SDN and OpenFlow for Dynamic Flex-Grid Optical Access and Aggregation Networks , 2014, Journal of Lightwave Technology.

[20]  E. Salvadori,et al.  Cognitive optical network testbed: EU project CHRON , 2015, IEEE/OSA Journal of Optical Communications and Networking.

[21]  Yuefeng Ji,et al.  Data center service localization based on virtual resource migration in software defined elastic optical network , 2015, OFC 2015.

[22]  P. N. Ji,et al.  Software defined optical network , 2012, The 2012 11th International Conference on Optical Communications and Networks (ICOCN).

[23]  Josep M. Fabrega,et al.  OFDM subcarrier monitoring using high resolution optical spectrum analysis , 2015 .

[24]  Elio Salvadori,et al.  Cognitive optical network testbed: EU project CHRON [Invited] , 2015 .

[25]  Lei Guo,et al.  Design and Implementation of the Routing Function in the NOX Controller for Software-Defined Networks , 2014 .

[26]  P. Winzer,et al.  Capacity Limits of Optical Fiber Networks , 2010, Journal of Lightwave Technology.

[27]  Miquel Garrich,et al.  Cognitive Methodology for Optical Amplifier Gain Adjustment in Dynamic DWDM Networks , 2016, Journal of Lightwave Technology.

[28]  Jianjun Yu,et al.  Cognitive optical networks: key drivers, enabling techniques, and adaptive bandwidth services , 2012, IEEE Communications Magazine.