Master Thesis Online
Multi-cloud observability with AI
Gómez Herrera, Maria Andrea Liliana 
Promotor(s) :
Debruyne, Christophe
Date of defense : 4-Sep-2023/5-Sep-2023 • Permalink : http://hdl.handle.net/2268.2/18181
Details
| Title : | Multi-cloud observability with AI |
| Author : | Gómez Herrera, Maria Andrea Liliana
|
| Date of defense : | 4-Sep-2023/5-Sep-2023 |
| Advisor(s) : | Debruyne, Christophe
|
| Committee's member(s) : | Marée, Raphaël
Leduc, Guy
Mokni, Marwa |
| Language : | English |
| Number of pages : | 84 |
| Keywords : | [en] cloud [en] observability |
| Discipline(s) : | Engineering, computing & technology > Computer science |
| Research unit : | Devoteam |
| Name of the research project : | ObservIT |
| Institution(s) : | Université de Liège, Liège, Belgique |
| Degree: | Master en science des données, à finalité spécialisée |
| Faculty: | Master thesis of the Faculté des Sciences appliquées |
Abstract
[en] In the face of the rapidly evolving landscape of IT solutions, observability has become more and more important in order to ensure the reliability and availability of cloud systems. With the recent changes in the way applications and infrastructures are developed, multi-cloud systems have been developed and therefore the need of a good observability for these systems has arose. With this the use of Artificial Intelligence techniques in order to automate IT processes has become popular. We introduce an approach based on distributed machine learning to predict incidents in a multi-cloud system segmented in different levels for a more in-depth analysis of the system.
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