基于集中式和分布式机器学习的可切片光网络QoT估计
Centralized and Distributed Machine Learning-Based QoT Estimation for Sliceable Optical Networks
摘要：
动态网络切片已经成为满足5G各种用例的有前途的基本框架。由于机器学习（ML）预计将在这些网络的有效控制和管理中发挥关键作用，在这项工作中，我们研究了基于ML的传输质量（QoT）估计问题下的动态网络切片上下文，其中每个切片必须满足不同的QoT要求。我们研究基于ML的QoT框架，目的是找到根据不同的QoT要求进行微调的QoT模型。集中式和分布式框架的检查和比较，根据其准确性和训练时间。我们发现，分布式的QoT模型优于集中式的QoT模型，特别是随着不同的QoT要求的数量增加。
英文摘要：
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.
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