Fault management based on machine learning

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Machine Learning (ML) brings many benefits for network operation. In this paper, basic ML concepts and its integration into existing network control and management planes are reviewed. Case studies covering fault management are illustrated.Peer ReviewedPostprint (author's final draft

Knowledge management in optical networks: architecture, methods, and use cases [Invited]

© [2019 Optical Society of America]. Users may use, reuse, and build upon the article, or use the article for text or data mining, so long as such uses are for non-commercial purposes and appropriate attribution is maintained. All other rights are reserved.Autonomous network operation realized by means of control loops, where prediction from machine learning (ML) models is used as input to proactively reconfigure individual optical devices or the whole optical network, has been recently proposed to minimize human intervention. A general issue in this approach is the limited accuracy of ML models due to the lack of real data for training the models. Although the training dataset can be complemented with data from lab experiments and simulation, it is probable that once in operation, events not considered during the training phase appear and thus lead to model inaccuracies. A feasible solution is to implement self-learning approaches, where model inaccuracies are used to re-train the models in the field and to spread such data for training models being used for devices of the same type in other nodes in the network. In this paper, we develop the concept of collective self-learning aiming at improving the model’s error convergence time as well as at minimizing the amount of data being shared and stored. To this end, we propose a knowledge management (KM) process and an architecture to support it. Besides knowledge usage, the KM process entails knowledge discovery, knowledge sharing, and knowledge assimilation. Specifically, knowledge sharing and assimilation are based on distributing and combining ML models, so specific methods are proposed for combining models. Two use cases are used to evaluate the proposed KM architecture and methods. Exhaustive simulation results show that model-based KM provides the best error convergence time with reduced data being shared.Peer ReviewedPostprint (author's final draft

A Bayesian Argumentation Framework for Distributed Fault Diagnosis in Telecommunication Networks

Traditionally, fault diagnosis in telecommunication network management is carried out by humans who use software support systems. The phenomenal growth in telecommunication networks has nonetheless triggered the interest in more autonomous approaches, capable of coping with emergent challenges such as the need to diagnose faults' root causes under uncertainty in geographically-distributed environments, with restrictions on data privacy. In this paper, we present a framework for distributed fault diagnosis under uncertainty based on an argumentative framework for multi-agent systems. In our approach, agents collaborate to reach conclusions by arguing in unpredictable scenarios. The observations collected from the network are used to infer possible fault root causes using Bayesian networks as causal models for the diagnosis process. Hypotheses about those fault root causes are discussed by agents in an argumentative dialogue to achieve a reliable conclusion. During that dialogue, agents handle the uncertainty of the diagnosis process, taking care of keeping data privacy among them. The proposed approach is compared against existing alternatives using benchmark multi-domain datasets. Moreover, we include data collected from a previous fault diagnosis system running in a telecommunication network for one and a half years. Results show that the proposed approach is suitable for the motivational scenario

Fault management based on machine learning

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Machine Learning (ML) brings many benefits for network operation. In this paper, basic ML concepts and its integration into existing network control and management planes are reviewed. Case studies covering fault management are illustrated.Peer Reviewe