Enabling Self-Driving Networks with Machine Learning

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 adapting 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 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 aims to enable self-driving networks by tackling the problem of the inherent lack of trust in ML models that empower it. To that end, we assess and scrutinize the decision-making process of ML-based 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. For that eval-uation, we propose a novel conversational interface called LUMI that allows operators to use natural language to describe how the network should behave. Second, we analyze and assess the accuracy and credibility of ML models' decisions to self-configure the network according to...