Automation of Switching in COADM using Machine Learning Algorithm
Divya Khanure1, B. Roja Reddy2
1Divya Khanure*, PG Student, Digital Communications, RV College of Engineering, Bengaluru, Karnataka, India.
2Dr. B. Roja Reddy, Associate Professor, Digital Communications, RV College of Engineering, Bengaluru, Karnataka, India.
Manuscript received on September 18, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 154-158 | Volume-8 Issue-12, October 2019. | Retrieval Number: L35051081219/2019©BEIESP | DOI: 10.35940/ijitee.L3505.1081219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper a Machine Learning (ML) algorithm has been proposed based on application in field of Optical Network, where in it makes use of large data set to learn, train the switching nodes and predicts the traffic in the network. Configurable Optical Add-Drop Multiplexer (COADM) are used as the switching nodes. Once prediction is done, the traffic at the node is directed to the next node automatically. This improves the performance in terms of efficiency and reduces the delay in the network due to automation.
Keywords: Machine Learning (ML), Support Vector Machine (SVM), Random Forest, k Nearest Neighbors (kNN), Configurable Optical Add-Drop Multiplexer (COADM).
Scope of the Article: Machine Learning