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. Specifically, 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 model accuracy, routing and spectrum allocation (RSA) accuracy, and CPU (training time) and RAM (memory) requirements. We show that the distributed QoT models outperform the centralized QoT model in accuracy and CPU usage. The RSA accuracy, i.e., measuring the accuracy of the models with regard to the QoT-aware RSA decisions, is sufficiently high for both frameworks. Regarding the RAM usage, as the distributed framework has to train in parallel several QoT models, it may require higher memory, especially as the number of diverse QoT requirements increases. This memory, however, tends to be reserved for a shorter period of time. Moreover, this work develops a dynamic multi-slice QoT-aware (RSA) framework that integrates the ML-based QoT models. The aim is to examine the network performance when the diverse QoT models are considered, as opposed to the state-of-the-art single-slice QoT-aware RSA approach where all connections/slices are provisioned according to a single QoT requirement. We show that the multi-slice QoT-aware RSA approach significantly improves network performance, a clear indicator that the commonly considered single-slice QoT-aware RSA approach may lead to connection overprovisioning.

中文翻译：

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基于分散式机器学习的可切片光网络QoT估计

动态网络切片已成为满足5G各种用例的有希望的基本框架。由于机器学习（ML）有望在这些网络的有效控制和管理中发挥关键作用，因此在这项工作中，我们研究了动态网络切片环境下基于ML的传输质量（QoT）估计问题，每个切片必须满足不同的QoT要求。具体而言，我们研究了基于ML的QoT框架，目的是找到根据不同QoT要求进行微调的QoT模型。根据模型的准确性，路由和频谱分配（RSA）的准确性以及CPU（训练时间）和RAM（内存）的要求，对集中式和分布式框架进行检查和比较。我们显示，分布式QoT模型在准确性和CPU使用率方面优于集中式QoT模型。对于两个框架，RSA准确性（即，根据QoT感知RSA决策测量模型的准确性）都足够高。关于RAM的使用，由于分布式框架必须并行训练几个QoT模型，因此可能需要更高的内存，尤其是随着各种QoT要求的增加。但是，此存储器倾向于保留较短的时间。此外，这项工作开发了一个动态的多层QoT感知（RSA）框架，该框架集成了基于ML的QoT模型。目的是在考虑各种QoT模型时检查网络性能，与最新的单片QoT感知RSA方法不同，后者根据一个QoT要求来提供所有连接/片。我们表明，支持QoT的多片RSA方法可以显着提高网络性能，这清楚地表明了通常认为的支持QoT的单片RSA方法可能导致连接预留过多。