Technological background

Network management is one of the most expensive tasks for telecommunications operating companies. Consequently, there is a tendency to delegate network management to the network itself. This is known as Autonomic Networking. But today, fault diagnosis is still a non-autonomous task. Traditionally, this process has been carried out by human experts supported by monitoring systems to detect alarms or symptoms. But even with those systems, the diagnostic task is essentially a manual process. The constant increase in network size and complexity makes fault diagnosis a critical business task that must be managed quickly and reliably. To carry it out, highly-skilled engineers are required, but even these people are not always able to cope with the growing heterogeneity and complexity of networks, since diagnosis is a difficult process, time-consuming and, therefore, it is an expensive task. Consequently, operators are aiming to fully automate fault diagnosis to reduce the cost of operation and improve the customer experience through the automated operation of standardized diagnostic processes.

Project Objectives

In order to solve the aforementioned limitations and advance in the development of automatic diagnostic techniques for SDN networks, this project aims to generate different monitoring and diagnostic techniques based on artificial intelligence techniques that allow the system to learn or adapt to the changes and evolution of the network. To do this, they plan to study and evaluate different evolutionary computing techniques or machine learning techniques. On the one hand, evolutionary computing techniques are very attractive and used in the literature to solve different optimization problems, which is interesting to evaluate and/or monitor the state of the network and thus be able to detect different anomalies in real time. On the other hand, machine learning techniques offer the system the ability to learn from the environment with previously collected data, which allows the system’s behavior to be adapted if the environment is modified by external factors. The development of this project will allow AXPE Consulting to enhance its position as a reference provider in the Telecommunications Sector, while helping us to consolidate our R&D area, as a lever for corporate development and a differentiating element. against our competition.