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Proposta de Tese de Arthur Selle Jacobs


Detalhes do Evento


Aluno: Arthur Selle Jacobs
Orientador: Prof. Dr. Lisandro Zambenedetti Granville

Título: Machine Learning for Self-Driving Networks
Linha de Pesquisa: Redes de Computadores.

Data: 01/12/2021
Horário: 13h30min.

Esta banca ocorrerá excepcionalmente de forma totalmente remota. Interessados em assistir a defesa poderão acessar a sala virtual através do link: https://join.skype.com/j5QT3z2BzkkY

Banca Examinadora:
Prof. Dr. Burkhard Stiller (University of Zurich)
Prof. Dr. Oscar Mauricio Caicedo Rendon (University of Cauca)
Prof. Dr. Luciano Paschoal Gaspary (UFRGS)

Presidente da Banca: Prof. Dr. Lisandro Zambenedetti Granville

Abstract: As modern networks grow in size and complexity, they also become increasingly prone to human errors. This trend has driven both industry and academia to try to automate management and control tasks, aiming to reduce human interaction with the network and human-made mistakes. Ideally, researchers envision a network design that is not only automatic (i.e., dependent of human instructions) but autonomous (i.e., capable of making its own decisions). Autonomous networking has been a goal sought for years, with many different concepts, designs and implementations, but it was never fully realized, mainly due to technological limitations. Recent advances in Artificial Intelligence (AI) and Machine Learning (ML) introduced a breath of fresh air into this concept, reemerging as the re-branded concept of self-driving networks, in view of its autonomous car counterparts. In broad terms, a self-driving network is an autonomous network capable of acting according to high-level intents from an operator and automatically adapt to changes in traffic and user behavior. To achieve that vision, a network would need to fulfill four major requirements: (i) understand high-level intents from an operator to dictate its behavior, (ii) monitor itself based on input intents, (iii) predict and identify changing patterns from monitored data and (iv) adapt itself to new behaviors without the intervention of an operator. As fulfilling the requirements of a self-driving network requires heavily relying on ML models to make decisions and classifications that directly impact the network, one particular issue becomes prominent with this design: trust. Applying ML to solve networking management tasks, such as the ones described above, has been a popular trend among researchers recently. However, despite the topic receiving much attention, industry operators have been reluctant to take advantage of such solutions, mainly because of the black-box nature of ML models which produce decisions without any explanation or reason as to which those decisions were made. Given the high-stakes nature of production networks, it becomes impossible to trust a ML model that may take system-breaking actions automatically, and most important to the scope of this thesis, a prohibitive challenge that must be addressed if a self-driving network design is ever to be achieved. The present thesis tackles the problem of the inherently lack of trust in ML models that empower self-driving networks by investigating the decision-making process of MLbased classifiers used to compose a self-driving network. First, we investigate and evaluate the accuracy and credibility of classifications made by ML models used to process high-level intents from the operator. Then, we analyze and assess the accuracy and credibility of decisions made by ML models used to self-configure the network according to monitored data. Finally, we investigate if there is a viable method to improve the trust of operators in the decision made by ML models in both management loops. Our results show that ML models that have been widely applied to solve networking problems have not been put under proper scrutiny, and can easily break when put under stress. Such models are, therefore, useless from a practical standpoint, and need to be corrected to fulfill their given tasks properly.