Machine Learning (ML) has been enjoying an unprecedented surge in applications that solve problems and enable automation in diverse domains. Primarily, this is due to the explosion in the availability of data, significant improvements in ML techniques, and advancement in computing capabilities. Undoubtedly, ML has been applied to various mundane and complex problems arising in network operation and management. There are various surveys on ML for specific areas in networking or for specific network technologies. This survey is original, since it jointly presents the application of diverse ML techniques in various key areas of networking across different network technologies. In this way, readers will benefit from a comprehensive discussion on the different learning paradigms and ML techniques applied to fundamental problems in networking, including traffic prediction, routing and classification, congestion control, resource and fault management, QoS and QoE management, and network security. Furthermore, this survey delineates the limitations, give insights, research challenges and future opportunities to advance ML in networking. Therefore, this is a timely contribution of the implications of ML for networking, that is pushing the barriers of autonomic network operation and management.

/pdf/a-comprehensive-survey-on-machine-learning-for-networking-3t5h3kbpe8.pdf

A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

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

Self-tuning optical systems are of growing importance in technological applications such as mode-locked fiber lasers. Such self-tuning paradigms require intelligent algorithms capable of inferring approximate models of the underlying physics and discovering appropriate control laws in order to maintain robust performance for a given objective. In this work, we demonstrate the first integration of a deep-learning (DL) architecture with model predictive control (MPC) in order to self-tune a mode-locked fiber laser. Not only can our DL-MPC algorithmic architecture approximate the unknown fiber birefringence, it also builds a dynamical model of the laser and appropriate control law for maintaining robust, high-energy pulses despite a stochastically drifting birefringence. We demonstrate the effectiveness of this method on a fiber laser that is mode-locked by nonlinear polarization rotation. The method advocated can be broadly applied to a variety of optical systems that require robust controllers.

Deep learning and model predictive control for self-tuning mode-locked lasers

pdf/machine-learning-for-intelligent-optical-networks-a-3ibvuglb5p.pdf

Machine learning for intelligent optical networks: A comprehensive survey

The interconnection of all things is developing a new diagram of future information networks. However, it is difficult to realize future applications with only one single technique. Collaboration between multiple advanced techniques is leading the way for the development of future information networks. Optical communication is an enabling technique to achieve high speed, long reach, and low latency communication, which plays an important role on the transformation of information networks. To achieve these advantages that caters to the characteristics of future information networks, collaboration of multiple advanced techniques with optical, which is called “optical plus X, could realize the vision of “all things connected with networks. In this paper, we focus on the collaboration between optical networks with other techniques, mainly discuss four representative aspects, which are “optical plus IP, “optical plus radio, “optical plus computing, and “optical plus AI. We discuss the challenges, timely works, and developing trends. Finally, we give the future visions for optical network towards a collaborative, converged and co-automatic optical network.

Towards converged, collaborative and co-automatic (3C) optical networks

In this paper, we propose a performance monitoring and failure prediction method in optical networks based on machine learning. The primary algorithms of this method are the support vector machine (SVM) and double exponential smoothing (DES). With a focus on risk-aware models in optical networks, the proposed protection plan primarily investigates how to predict the risk of an equipment failure. To the best of our knowledge, this important problem has not yet been fully considered. Experimental results showed that the average prediction accuracy of our method was 95% when predicting the optical equipment failure state. This finding means that our method can forecast an equipment failure risk with high accuracy. Therefore, our proposed DES-SVM method can effectively improve traditional risk-aware models to protect services from possible failures and enhance the optical network stability.

Failure prediction using machine learning and time series in optical network.

Millions of alarms in the optical layer may appear in optical transport networks every month, which brings great challenges to network operation, administration and maintenance. In this paper, we deal with this problem and propose a method of alarm pre-processing and correlation analysis for this network. During the alarm pre-processing, we use the method of combined time series segmentation and time sliding window to extract the alarm transactions, and then we use the algorithm of combined $K$ -means and back propagation neural network to evaluate the alarm importance quantitatively. During the alarm correlation analysis, we modify a classic rule mining algorithm, i.e., Apriori algorithm, into a Weighted Apriori to find the high-frequency chain alarm sets among the alarm transactions. Through the actual alarm data from the record in the optical layer of a provincial backbone of China Telecom, we conducted experiments and the results show that our method is able to perform effectively the alarm compressing, alarm correlating, and chain alarm mining. By parameter adjustment, the alarm compression rate is able to vary from 60% to 90% and the average fidelity of chain alarm mining keeps around 84%. The results show our approach and method is promising for trivial alarm identifying, chain alarm mining, and root fault locating in existing optical networks.

/pdf/dealing-with-alarms-in-optical-networks-using-an-intelligent-127smrpbia.pdf

Dealing With Alarms in Optical Networks Using an Intelligent System

A method based on machine learning and association rules mining was proposed. Experiments show that the approach is practical in alarm compression, and the performance of KNA method is better than the classical Apriori algorithm.

Alarm Compression Based on Machine Learning and Association Rules Mining in Optical Networks

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Papers

Failure prediction using machine learning and time series in optical network.

Dealing With Alarms in Optical Networks Using an Intelligent System

Alarm Compression Based on Machine Learning and Association Rules Mining in Optical Networks