Today’s telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users’ behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, machine learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing, and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude this paper proposing new possible research directions.

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An Overview on Application of Machine Learning Techniques in Optical Networks

In recent years, with the rapid development of current Internet and mobile communication technologies, the infrastructure, devices and resources in networking systems are becoming more complex and heterogeneous. In order to efficiently organize, manage, maintain and optimize networking systems, more intelligence needs to be deployed. However, due to the inherently distributed feature of traditional networks, machine learning techniques are hard to be applied and deployed to control and operate networks. Software defined networking (SDN) brings us new chances to provide intelligence inside the networks. The capabilities of SDN (e.g., logically centralized control, global view of the network, software-based traffic analysis, and dynamic updating of forwarding rules) make it easier to apply machine learning techniques. In this paper, we provide a comprehensive survey on the literature involving machine learning algorithms applied to SDN. First, the related works and background knowledge are introduced. Then, we present an overview of machine learning algorithms. In addition, we review how machine learning algorithms are applied in the realm of SDN, from the perspective of traffic classification, routing optimization, quality of service/quality of experience prediction, resource management and security. Finally, challenges and broader perspectives are discussed.

A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges

Machine learning (ML) has disrupted a wide range of science and engineering disciplines in recent years. ML applications in optical communications and networking are also gaining more attention, particularly in the areas of nonlinear transmission systems, optical performance monitoring, and cross-layer network optimizations for software-defined networks. However, the extent to which ML techniques can benefit optical communications and networking is not clear and this is partly due to an insufficient understanding of the nature of ML concepts. This paper aims to describe the mathematical foundations of basic ML techniques from communication theory and signal processing perspectives, which in turn will shed light on the types of problems in optical communications and networking that naturally warrant ML use. This will be followed by an overview of ongoing ML research in optical communications and networking with a focus on physical layer issues.

An Optical Communication's Perspective on Machine Learning and Its Applications

While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently, there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services, such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The growing interest in applying unsupervised learning techniques in networking stems from their great success in other fields, such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). In addition, unsupervised learning can unconstrain us from the need for labeled data and manual handcrafted feature engineering, thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting recent advancements in unsupervised learning techniques, and describe their applications in various learning tasks, in the context of networking. We also provide a discussion on future directions and open research issues, while identifying potential pitfalls. While a few survey papers focusing on applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in the literature. Through this timely review, we aim to advance the current state of knowledge, by carefully synthesizing insights from previous survey papers, while providing contemporary coverage of the recent advances and innovations.

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Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

Sixth-generation (6G) mobile networks will have to cope with diverse threats on a space-air-ground integrated network environment, novel technologies, and an accessible user information explosion. However, for now, security and privacy issues for 6G remain largely in concept. This survey provides a systematic overview of security and privacy issues based on prospective technologies for 6G in the physical, connection, and service layers, as well as through lessons learned from the failures of existing security architectures and state-of-the-art defenses. Two key lessons learned are as follows. First, other than inheriting vulnerabilities from the previous generations, 6G has new threat vectors from new radio technologies, such as the exposed location of radio stripes in ultra-massive MIMO systems at Terahertz bands and attacks against pervasive intelligence. Second, physical layer protection, deep network slicing, quantum-safe communications, artificial intelligence (AI) security, platform-agnostic security, real-time adaptive security, and novel data protection mechanisms such as distributed ledgers and differential privacy are the top promising techniques to mitigate the attack magnitude and personal data breaches substantially.

Security and privacy for 6G: A survey on prospective technologies and challenges

In general, humans follow a routine with highly predictable daily movements. For instance, we commute from home to work on a daily basis and visit a selected set of places for commercial and recreational purposes during the nights and weekends. The use of mobile phones increases when commuting via public transportation, during lunch breaks, and at night. Such regular behavior creates predictable spatiotemporal fluctuations of traffic patterns. In this paper, we introduce a matheuristic for dynamic optical routing, which can be implemented as an application into a software-defined mobile carrier network. We use machine learning to predict tidal traffic variations in a mobile metro-core network, which allows us to solve off-line mixed-integer linear programming instances of an optical routing (and wavelength) assignment optimization problem. The optimal results are used to favor near-optimal on-line routing decisions. Results demonstrate the effectiveness of our on-line methodology, with results that match almost perfectly the behavior of a network that performs optical routing reconfiguration with a perfect, oracle-like traffic prediction and the solution of an optimization problem.

Matheuristic with machine-learning-based prediction for software-defined mobile metro-core networks

By predicting the traffic load on network links, a network operator can effectively pre-dispose resource-allocation strategies to early address, e.g., an incoming congestion event. Traffic loads on different links of a telecom is know to be subject to strong correlation, and this correlation, if properly represented, can be exploited to refine the prediction of future congestion events. Machine Learning (ML) represents nowadays the state-of-the-art methodology for discovering complex relations among data. However, ML has been traditionally applied to data represented in the Euclidean space (e.g., to images) and it may not be straightforward to effectively employ it to model graph-stuctured data (e.g., as the events that take place in telecom networks). Recently, several ML algorithms specifically designed to learn models of graph-structured data have appeared in the literature. The main novelty of these techniques relies on their ability to learn a representation of each node of the graph considering both its properties (e.g., features) and the structure of the network (e.g., the topology). In this paper, we employ a recently-proposed graph-based ML algorithm, the Diffusion Convolutional Recurrent Neural Network (DCRNN), to forecast traffic load on the links of a real backbone network. We evaluate DRCNN's ability to forecast the volume of expected traffic and to predict events of congestion, and we compare this approach to other existing approaches (as LSTM, and Fully-Connected Neural Networks). Results show that DCRN outperforms the other methods both in terms of its forecasting ability (e.g., MAPE is reduced from 210% to 43%) and in terms of the prediction of congestion events, and represent promising starting point for the application of DRCNN to other network management problems.

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Network Traffic Prediction based on Diffusion Convolutional Recurrent Neural Networks

In recent years, researchers realized that the analysis of traffic datasets can reveal valuable information for the management of mobile and metro-core networks. That is getting more and more true with the increase in the use of social media and Internet applications on mobile devices. In this work, we focus on deep learning methods to make prediction of traffic matrices that allow us to proactively optimize the resource allocations of optical backbone networks. Recurrent Neural Networks (RNNs) are designed for sequence prediction problems and they achieved great results in the past years in tasks like speech recognition, handwriting recognition and prediction of time series data. We investigated a particular type of RNN, the Gated Recurrent Units (GRU), able to achieve great accuracy (< 7.4 of mean absolute error). Then, we used the predictions to dynamically and proactively allocate the resources of an optical network. Comparing numerical results of static vs. dynamic allocation based on predictions, we can estimate a saving of 66.3% of the available capacity in the network, managing unexpected traffic peaks.

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Deep Learning-Based Traffic Prediction for Network Optimization

Recently, machine learning (ML) has attracted the attention of both researchers and practitioners to address several issues in the optical networking field. This trend has been mainly driven by the huge amount of available data (i.e., signal quality indicators, network alarms, etc.) and to the large number of optimization parameters which feature current optical networks (such as, modulation format, lightpath routes, transport wavelength, etc.). In this paper, we leverage the techniques from the ML discipline to efficiently accomplish the routing and wavelength assignment (RWA) for an input traffic matrix in an optical WDM network. Numerical results show that near-optimal RWA can be obtained with our approach, while reducing computational time up to 93% in comparison to a traditional optimization approach based on integer linear programming. Moreover, to further demonstrate the effectiveness of our approach, we deployed the ML classifier into an ONOS-based software defined optical network laboratory testbed, where we evaluate the performance of the overall RWA process in terms of computational time.

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Machine Learning-Based Routing and Wavelength Assignment in Software-Defined Optical Networks

With the advent of 5G technology, we are witnessing the development of increasingly bandwidth-hungry network applications, such as enhanced mobile broadband, massive machine-type communications and ultra-reliable low-latency communications. Software Defined Networking (SDN), Network Function Virtualization (NFV) and Network Slicing (NS) are gaining momentum not only in research but also in IT industry representing the drivers of 5G. NS is an approach to network operations allowing the partition of a physical topology into multiple independent virtual networks, called network slices (or slices). Within a single slice, a set of Service Function Chains (SFCs) is defined and the network resources, e.g. bandwidth, can be provisioned dynamically on demand according to specific Quality of Service (QoS) and Service Level Agreement (SLA) requirements. Traditional schemes for network resources provisioning based on static policies may lead to poor resource utilization and suffer from scalability issues. In this article, we investigate the application of Reinforcement Learning (RL) for performing dynamic SFC resources allocation in NFV-SDN enabled metro-core optical networks. RL allows to build a self-learning system able to solve highly complex problems by employing RL agents to learn policies from an evolving network environment. In particular, we build an RL system able to optimize the resources allocation of SFCs in a multi-layer network (packet over flexi-grid optical layer). The RL agent decides if and when to reconfigure the SFCs, given state of the network and historical traffic traces. Numerical simulations show significant advantages of our RL-based optimization over rule-based optimization design.

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Sebastian Troia

Papers

Matheuristic with machine-learning-based prediction for software-defined mobile metro-core networks

Network Traffic Prediction based on Diffusion Convolutional Recurrent Neural Networks

Deep Learning-Based Traffic Prediction for Network Optimization

Machine Learning-Based Routing and Wavelength Assignment in Software-Defined Optical Networks

Reinforcement Learning for Service Function Chain Reconfiguration in NFV-SDN Metro-Core Optical Networks