Assessing transmission excellence and flow detection based on Machine Learning

摘要

Excellence in transmission can be assessed in optical transport networks before providing any additional connections or upgrading the connections. Generally, the Physical Layer Model (PLM) is used to assess the transmission quality which has high probability in uncertainty and inaccuracy due to the circumstances of physical layer. The network efficiency is directly proportional to the margins. If the margins getting increases in the PLM, the efficiency of the network decreases. Maintaining the excellence in transmission is the biggest challenge when the margins getting increased. Other significant factors for excellence in transmission is scalable, minimum latency with maximum speed and energy efficient. Photonic switching is a hopeful solution for handling these challenges. Machine learning technique is proposed to assess the excellence of transmission and flow detection. ML-E and Precedence based scheduling algorithms are proposed for excellence of transmission and flow detection respectively. The proposed techniques justify variations, uncertainties in kits like fiber dilution, dispersion and optimizes PSON (packet switched optical network). Simulation results are demonstrated and the proposed work results indicates that it can outperform a benchmark in all aspects.