Centralized and Distributed Machine Learning-Based QoT Estimation for Sliceable Optical Networks

TL;DR

This paper investigates machine learning-based QoT estimation methods for dynamic network slicing in 5G, comparing centralized and distributed frameworks to improve accuracy and training efficiency.

Contribution

It introduces and compares centralized and distributed ML-based QoT estimation frameworks tailored for diverse 5G network slices, highlighting the advantages of distributed models.

Findings

Distributed QoT models outperform centralized ones as diversity increases.

Distributed models have faster training times.

ML-based QoT estimation enhances network slicing management.

Abstract

Dynamic network slicing has emerged as a promising and fundamental framework for meeting 5G's diverse use cases. As machine learning (ML) is expected to play a pivotal role in the efficient control and management of these networks, in this work we examine the ML-based Quality-of-Transmission (QoT) estimation problem under the dynamic network slicing context, where each slice has to meet a different QoT requirement. We examine ML-based QoT frameworks with the aim of finding QoT model/s that are fine-tuned according to the diverse QoT requirements. Centralized and distributed frameworks are examined and compared according to their accuracy and training time. We show that the distributed QoT models outperform the centralized QoT model, especially as the number of diverse QoT requirements increases.