As a consequence of increased complexity, technical requirements of services and expectations of end-users, telecommunication networks and systems must still increase their efficiency. Research currently focuses on introducing automation and intelligence into network control and management. Neural Networks form a class of Machine Learning techniques utilizing the supervised learning paradigm. The class is mature and comprises a significant number of algorithms. As a result, Neural Networks-based solutions are widely applicable, also in the context of communications and networking. The aim of this tutorial is twofold. Firstly, to provide fundamentals regarding the Neural Network (NN) method. Secondly, to comprehensively study the examples of applying NN-based solutions to solve problems in different aspects of communications and networking. Studies are supplemented with additional explanations, figures and critical considerations, including pros and cons of using selected methods for particular purposes. This part uniquely complements the tutorial one and facilitates in-depth understanding of NN. Based on the conducted studies, we draw a comparative analysis, summaries and expected future research topics and challenges of using NN in communications and networking.

In this paper, we explore the applicability of selected machine learning models to classify incoming flows as elephants or mice on the first packet, using IP and transport layer headers (5-tuple). We show that traditional metrics such as accuracy or F1-score are inadequate for assessing performance in traffic engineering and QoS applications unless compared at the same traffic coverage. Among the classifiers analyzed, Histogram-based Gradient Boosting with octets-transformed input data provides the best performance, reducing flow operations by a factor of 36.49 and the average number of flow table entries by 16.35, while covering 80% of the traffic.

This paper investigates the application of tree-based machine learning classifiers for flow-based traffic engineering, focusing on the binary classification of IP network flows into mice (short flows) and elephants (long flows) using 5-tuple header fields from the first packet. Unlike prior studies on network flow classification, our analysis uses performance metrics normalized by traffic coverage, ensuring relevance for traffic engineering and QoS applications. We also evaluate models within the constraints of programmable switching hardware, such as the Intel Tofino P4 chip. Our findings show that such constrained models can achieve high accuracy while performing inference at line rate in the dataplane. Additionally, we reveal a trade-off between tree depth and input format, with bit transformations enabling more efficient feature extraction at lower depths. Our results show that optimal tree depths range from 15 to 25 levels, depending on the input format. The most effective model employs extremely randomized trees with bit-transformed input and trees of depth 20.

Quick and accurate identification of the largest flows in the network would allow for the management of most traffic using dedicated, flow-specific routes and policies, thereby significantly reducing the overall number of entries in switch flow tables. Our analysis focuses on utilizing neural networks to classify elephant flows based on the first packet using 5-tuple packet header fields. The findings indicate that with simple neural networks comprising solely linear layers, it is possible to accurately detect elephant flows at their inception, thereby reducing the number of flow table entries – by up to a factor of 15 – while still effectively covering 80% of the network traffic with individual flow entries.

Rapid and precise detection of significant data streams within a network is crucial for efficient traffic management. This study leverages the TabNet deep learning architecture to identify large-scale flows, known as elephant flows, by analyzing the information in the 5-tuple fields of the initial packet header. The results demonstrate that employing a TabNet model can accurately identify elephant flows right at the start of the flow and makes it possible to reduce the number of flow table entries by up to 20 times while still effectively managing 80% of the network traffic through individual flow entries. The model was trained and tested on a comprehensive dataset from a campus network, demonstrating its robustness and potential applicability to varied network environments.

With the global Internet traffic continuously growing, network operators face more and more challenges related to the management of their networks. Efficient utilization of the available network resources becomes crucial to maintain the desired Quality of Service level and control the upsurge in operational expenses. The combination of the Software-Defined Networking concept with the multi-layer network architecture can simplify the process of control and management of the network, its layers, and resources. In this paper, we propose a solution to enhance resource utilization in software-defined multi-layer optical networks. The proposed solution takes the advantages of the Software-Defined Networking, Flow-Aware Multi-Topology Adaptive Routing, and Automatic Hidden Bypasses mechanisms to ensure simultaneous, multi-path data transmissions in both IP and optical layers. The Software-Defined Networking controller manages both mentioned mechanisms, selects the best possible bypass, and allocates lightpaths to ensure that the optical spectral efficiency is optimal. The evaluation shows that the proposed solution for multi-layer software-defined network increases the overall network performance and resource utilization.