Fault diagnosis for ip-based network with real-time conditions

Abstract

Background: fault diagnosis techniques have been based on many paradigms, which derive from diverse áreas and have different purposes: obtaining a representation model of the network for fault localization, selecting optimal probe sets for monitoring network devices, reducing fault detection time, and detecting faulty components in the network. Although there are several solutions for diagnosing network faults, there are still challenges to be faced: a fault diagnosis solution needs to always be available and able enough to process data timely, because stale results inhibit the quality and speed of informed decision-making. Also, there is no non-invasive technique to continuously diagnose the network symptoms without leaving the system vulnerable to any failures, nor a resilient technique to the network's dynamic changes, which can cause new failures with different symptoms. Aims: this thesis aims to propose a model for the continuous and timely diagnosis of IP-based networks faults, independent of the network structure, and based on data analytics techniques.