Abstract
Optical networking is fast evolving towards the applications of the Software-defined Networking (SDN) paradigm down to the (Wavelength-division Multiplexing) WDM transport layer for cost-effective and flexible infrastructure management. Optical SDN requires each network element's software abstraction to enable full control by the centralized network controller. Nowadays, modern network elements, especially photonic switching systems, are developed by exploiting the fast-emerging technology of Photonic Integrated Circuit (PIC) that consists of complex fabrics of elementary units that can be driven individually using a large set of elementary controls. In this work, we focus on modeling the elementary control states of the topological structures behind PIC N ×N switches under a fully blind approach based on Machine Learning (ML) techniques. The ML agent's training and testing datasets are obtained synthetically by software simulation of the photonic switch structure. The proposed technique's scalability and accuracy are validated by considering different dimensions N and applying it to two different switching topologies: the Honey-Comb Rearrangeable Optical Switch and the Beneš network. Excellent results in terms of prediction of the control states are achieved for both of the considered topologies.
Highlights
T HE ever-increasing demand for global internet traffic and evolving concepts of connectivity demand for flexible and dynamic networking at every layer
This work focuses on the abstraction of control states of optical switches based on Photonic Integrated Circuit (PIC) with a structure-agnostic approach based on Machine Learning (ML) techniques
IV, we describe the structure of the proposed ML agent, showing how it is trained on the datasets of different controls and output signals permutations in order to predict the control states of internal switching elements
Summary
T HE ever-increasing demand for global internet traffic and evolving concepts of connectivity demand for flexible and dynamic networking at every layer. Generalpurpose routing algorithms do not provide scalable solutions, as the computational complexity increases rapidly [11], [12], [13] This is caused by the exponential growth of the control states Nst in the network, which depends on the number of switches M as Nst = 2M. We present a novel topology-agnostic blind approach exploiting an ML agent to predict the control states of the N ×N photonic switch with an arbitrary and potentially unknown internal structure.
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