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.

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

This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks.

A Survey of Self Organisation in Future Cellular Networks

In this paper, a survey of the literature of the past 15 years involving machine learning (ML) algorithms applied to self-organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of self-organizing networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this paper also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future.

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A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

Fault diagnosis is useful in helping technicians detect, isolate, and identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various uncertainty problems. This model is increasingly utilized in fault diagnosis. This paper presents bibliographical review on use of BNs in fault diagnosis in the last decades with focus on engineering systems. This work also presents general procedure of fault diagnosis modeling with BNs; processes include BN structure modeling, BN parameter modeling, BN inference, fault identification, validation, and verification. The paper provides series of classification schemes for BNs for fault diagnosis, BNs combined with other techniques, and domain of fault diagnosis with BN. This study finally explores current gaps and challenges and several directions for future research.

Bayesian Networks in Fault Diagnosis

Transient fault (TF) and intermittent fault (IF) of complex electronic systems are difficult to diagnose. As the performance of electronic products degrades over time, the results of fault diagnosis could be different at different times for the given identical fault symptoms. A dynamic Bayesian network (DBN)-based fault diagnosis methodology in the presence of TF and IF for electronic systems is proposed. DBNs are used to model the dynamic degradation process of electronic products, and Markov chains are used to model the transition relationships of four states, i.e., no fault, TF, IF, and permanent fault. Our fault diagnosis methodology can identify the faulty components and distinguish the fault types. Four fault diagnosis cases of the Genius modular redundancy control system are investigated to demonstrate the application of this methodology.

A Dynamic-Bayesian-Network-Based Fault Diagnosis Methodology Considering Transient and Intermittent Faults

This paper presents an automated diagnosis in troubleshooting (TS) for Universal Mobile Telecommunications System (UMTS) networks using a Bayesian network (BN) approach. An automated diagnosis model is first described using the Naive Bayesian Classifier. To increase the performance of the diagnosis model, the entropy minimization discretization (EMD) method is incorporated into the model to select optimal segments for the discretization of the input symptoms. In the first phase, the diagnosis model is constructed using a dynamic simulator. The simulator TS platform allows generation of a large amount of data required to study the relations between faults and symptoms. In the second phase, the diagnosis model is adapted to a real UMTS network using counters and key performance indicators (KPIs) recovered from an Operations and Maintenance Center (OMC). Results for the automated diagnosis using both network simulator and real UMTS network measurements illustrate the efficiency of the proposed TS approach and its importance to mobile network operators.

Automated Diagnosis for UMTS Networks Using Bayesian Network Approach

Troubleshooting (TS) in UMTS networks are basic management tasks required to guarantee efficient usage of the network infrastructure. This paper presents a methodology for automating TS tasks based on Bayesian Networks (BN). In a first learning phase, data relating symptoms and alarms to faults in the network is extracted and used to create the TS model. In a second phase, symptoms and alarms are used to identify faults with the highest probabilities. A TS case study using a dynamic system simulator illustrates the effectiveness of the proposed approach.

R.M. Khanafer

Papers

Automated Diagnosis for UMTS Networks Using Bayesian Network Approach

A Bayesian Approach for Automated Troubleshooting for UMTS Networks