In this paper, we address the challenge of anomaly detection in multivariate time series data related to environments requiring stringent guarantees, notably in 5G and beyond 5G systems promising high reliability and quality. Traditional approaches to anomaly detection, including supervised, unsuper-vised, and self-supervised methods often struggle with the diver-sity and unpredictability of real-world anomalies. To overcome these limitations, we propose a novel dual forecasting model approach, DREAM (Dual foREcAsting Model Anomaly Detection), which leverages both normal and unlabeled data to enhance detection accuracy. Our approach involves training two distinct models: one on normal behavior and the other on mixed behavior, and then comparing their outputs to identify anomalies. We also introduce new evaluation method, addressing the shortcomings of traditional point-wise evaluation. Our experiments, with multiple networked system datasets, demonstrate that DREAM outper-forms traditional forecasting-based approaches, including when used in a hybrid manner with traditional forecasting algorithms.

Network automation is crucial for improving network performance. Commit messages describes the different actions of the modification of network configuration files and deployments. This paper presents experiments and studies on automated commit message generation in the deployment of 5G networks. We extracted data from repositories of various projects engineered in Orange’s 5G network. We then developed five prompts for experiments to identify the most suitable methods for this task. To select large language models, we used an in-house GPT-4 interface provided by Orange, and locally deployed popular large models such as Llama3, Mistral. We used both automated and human evaluation methods, selecting BLEU, ROUGE, and METEOR as our metrics for automated assessment. Our experiments shows that commit messages for configuration files generated by Large Language Models (LLMs) have better scores when using one-shot and Retrieval-Augmented Generation (RAG) technologies, for messages generated both by humans and bots.

The 5G system should be flexible to cope with diversity of functions and infrastructure, in an efficient way while supporting a wide range of services associated with a set of requirements through Service Function Chaining (SFC). Fault management plays a paramount role to fulfill the SFC requirements towards reliability of telecommunication networks. Model-based (MB) approaches are knowledge-based systems that reason about a system from an explicit representation of its structure and functional behavior through a dependency model. MB techniques solve novel diagnosis problems and provide explanations for their decisions. However, current MB methods suffer from some limitations due to network virtualization challenges: lack of network visibility and dynamic topologies. To tackle those limitations, we propose a self-modeling approach and an active diagnosis process for virtual networks that considers two types of knowledge to build the model: acquired knowledge and learned knowledge provided by fault injection to expand and validate the proposed model. The experimental results from their application to a real-world virtual IP Multimedia Subsystem (vIMS) use case show that the self-modeling and the active diagnosis procedures are effective in determining the root cause(s) of a failure and explaining fault propagation.

Model based machine learning (MBML) techniques solve novel diagnosis problems and provide explanations for their decisions. However, current MBMLs suffer some limitations, since virtualization of network brings new challenges such as the dynamic topology and elasticity. Those limitations include the high dependency on previous knowledge and the difficulty to represent the model. To face those limitations, we propose SAKURA: a root cause analysis framework for the virtual Ip Multimedia Subsystem (vIMS). SAKURA is composed of a self-modeling and a constraints solver.

The advent of 5G and the ever increasing stringent requirements in terms of bandwidth, latency, and quality of service pushes the boundaries of what is feasible with legacy Mobile Network Operators' technologies. Network Functions Virtualization (NFV) is a promising attempt at solving some of these challenges that is widely adopted by the industry and specified by the standardization bodies. In essence, NFV is about running Network Functions (NFs) as virtualized workloads on commodity hardware. This may optimize deployment costs and simplify the lifecycle management of NFs, but it introduces new fault management challenges and issues. In this paper, we propose a comprehensive state of the art of fault management techniques. We address the impact of virtualization in fault management. We propose a new classification of the recent fault management research achievements in the network virtualization environments and compare their major contributions and shortcomings.