Microservices-Based Autonomous Anomaly Detection for Mobile Network Observability

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Journal Title
Journal ISSN
Volume Title
Perustieteiden korkeakoulu | Master's thesis
Date
2023-10-09
Department
Major/Subject
Data Science
Mcode
SCI3115
Degree programme
Master's Programme in ICT Innovation
Language
en
Pages
122+7
Series
Abstract
In modern telecommunication networks, network observability entails the use of diverse data sources to understand the state and behavior of the network, and its ability to provide the required service and user experience. Because of the vast amounts of data collection and transmission involved in this process, the network's performance is negatively impacted, and it can become difficult for network operators to identify the occurrence of problematic behavior before it is too late. To enable a more efficient form of data collection and aid in diagnostic operations, this thesis aims to develop an autonomous anomaly detection system for time series data. The system is to be developed as a microservices-based solution, to be integrated with a software-defined networking controller platform developed at \textit{Ericsson}. This thesis describes the extensive experimentation process conducted during the development of this system, including various methods of data processing, time series clustering, and anomaly detection. The resulting system is a highly customizable and scalable product, supported by modern and reliable anomaly detection models. The system is capable of detecting several different kinds of anomalies in an arbitrary number of mobile network monitoring metrics and can be easily configured to fit the specific needs of each customer.
Description
Supervisor
Jung, Alex
Thesis advisor
Bergenwall, Thomas
Keywords
anomaly detection, outlier detection, time series, software-defined networking, microservices
Other note
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