BACKGROUND:Fault diagnosis techniques have been based on many paradigms, which derive from diverse areas 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.
METHOD(S):This research's point of departure was the hypothesis of a fault propagation phenomenon that allows the observation of failure symptoms at a higher network level than the fault origin. Thus, for the model's construction, monitoring data was collected from an extensive campus network in which impact link failures were induced at different instants of time and with different duration. These data correspond to widely used parameters in the actual management of a network. The collected data allowed us to understand the faults' behavior and how they are manifested at a peripheral level.Based on this understanding and a data analytics process, the first three modules of our model, named PALADIN, were proposed (Identify, Collection and Structuring), which define the data collection peripherally and the necessary data pre-processing to obtain the description of the network's state at a given moment. These modules give the model the ability to structure the data considering the delays of the multiple responses that the network delivers to a single monitoring probe and the multiple network interfaces that a peripheral device may have. Thus, a structured data stream is obtained, and it is ready to be analyzed. For this analysis, it was necessary to implement an incremental learning framework that respects networks' dynamic nature. It comprises three elements, an incremental learning algorithm, a data rebalancing strategy, and a concept drift detector. This framework is the fourth module of the PALADIN model named Diagnosis.In order to evaluate the PALADIN model, the Diagnosis module was implemented with 25 different incremental algorithms, ADWIN as concept-drift detector and SMOTE (adapted to streaming scenario) as the rebalancing strategy. On the other hand, a dataset was built through the first modules of the PALADIN model (SOFI dataset), which means that these data are the incoming data stream of the Diagnosis module used to evaluate its performance.The PAL...
