Name: Vitor Fontana Zanotelli
Type: MSc dissertation
Publication date: 20/05/2022

Namesort descending Role
Giovanni Ventorim Comarela Co-advisor *
Magnos Martinello Advisor *

Examining board:

Namesort descending Role
Antonio Augusto de Aragão Rocha External Examiner *
Giovanni Ventorim Comarela Co advisor *
Magnos Martinello Advisor *
Vinicius Fernandes Soares Mota Internal Examiner *

Summary: The Ipê Network is fundamental to the Brazilian scientific community, being responsible for
interconnecting universities and research centers throughout the country. The network also
presents international connections, allowing Brazilian cooperation with foreign research
entities. It is an extensive network, producing a high volume of data and presenting
challenges related to its operation. This work is divided into two parts, the first being
responsible for presenting an analysis of the network through the characterization of failure
behavior. The second attempt consists of constructing learning models to predict the
occurrence of failures, allowing for planning on how to mitigate the problems caused by
the occurrence of failures. Data is collected through the Via Ipê web app and corresponds
to the period of November 2020 through November 2021. The problem is modeled as
supervised learning for binary classification and recurrent neural networks (LSTMs) are
used. The Ipê Network presents heterogeneous behavior, manifesting great variety on the
dependability of its connectivity services in its different PoPs. Different models considering
the network’s characteristics are proposed to deal with this scenario, from more general to
more restricted models. The models’ performance metrics reveal different types of failures,
complementing the initial analysis of the data. The problem is shown to be difficult, but
the proposed methodology shows promise, with acceptable results in some cases.

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