Towards Autonomous Computer Networks in Support of Critical Systems

A recent trend dictating evolution of management and orchestration of computer networks is constituted by the softwarization and virtualization of them, which have drastically simplified the deployment and real-time reconfiguration of network functions, allowing them to continuously adapt and to deal with dynamic demands in an automated way. Alongside, recent management and orchestration approaches for softwarized networks employ Artificial Intelligence (AI) and Machine Learning (ML) to further reduce reaction time and improve the accuracy of decisions, where the network operations can be automated to the point of realizing autonomous driving networks. However, while automating operations can improve the overall system (it is acknowledged that 70% of network faults are caused by manual errors), AI/ML methods are not the panaceas, and we are still far from having a fully operating and efficient automated architecture. In this dissertation, we present a novel class of software network solutions that share the goal of enabling intelligent and autonomous computer networks, exploring how to exploit the power of AI/ML to handle the growing complexity of critical systems. We start with a new network management scheme for adaptive routing and autonomous scaling of virtual network resources. Then, acting on the hosts, we propose to adjust the TCP congestion control with a ML-based solution, whose goal is to select the proper congestion window learning from end-to-end features and (when available) network signals. We believe that the proposed solutions, and their combination, can lay the foundation for automated systems that better suit modern edge environments and cellular networks by providing unprecedented flexibility and adaptation to even unseen and unknown network conditions.