Multi-agent graph learning-based optimization and its applications to computer networks

Guillermo Bernárdez

Abstract: English) In the wake of a digital revolution, contemporary society finds itself entrenched in an era where network applications' demands surpass the capabilities of conventional network management solutions. This dissertation navigates through the intricacies of modern networked environments, where traditional management approaches are falling short due to emerging applications like augmented and virtual reality, holographic telepresence, and vehicular networks, demanding ultra-low latency and robust adaptability. These evolving networks form the backbone of modern society, sustaining numerous vital services but posing elevated complexity and operational hurdles for Internet Service Providers (ISPs) and network operators. Amidst this complexity, the need for innovative solutions to optimize and manage today's networks is more pronounced than ever. A central proposition of this dissertation is the MAGNNETO framework, a groundbreaking Machine Learning (ML) based initiative that stands for Multi-Agent Graph Neural Network Optimization. This framework is at the heart of the endeavour to facilitate distributed optimization in networked scenarios. By integrating a Graph Neural Network (GNN) architecture into a Multi-Agent Reinforcement Learning (MARL) setting, it instigates a fully distributed optimization process and capitalizes on the inherent distributed nature of networked environments, hence potentially addressing scalability issues and facilitating real-time applications. This initiative is adaptable, offering versatility in addressing various use cases and showcasing robustness to meet the challenging requisites of real-world applications. A substantial contribution of this work is the successful implementation of MAGNNETO across different relevant networked cases, prominently focusing on two highly impactful scenarios within the computer network field. Initially, it re-examines the pivotal issue of Traffic Engineering (TE) optimization in ISP networks. With the goal of curtailing network congestion, MAGNNETO-TE is introduced, a variant of the framework specifically devised to minimize maximum link utilization in these networks. Remarkably, this adaptation heralds a paradigm shift by equalling the performance of traditional state-of-the-art TE optimizers but at a fraction of the execution cost. Moreover, the research explores the complex sphere of Congestion Control (CC) in Datacenter Networks (DCN), another critical service in our current digital world that is characterized by dynamic traffic patterns and stringent low-latency prerequisites. Here, MAGNNETO-CC emerges as a potent solution, offering an offline, distributed strategy that harmonizes with widely deployed CC protocols, surpassing other state-of-the-art ML-based CC methodologies and prevailing static CC configurations in performance. Looking ahead, the dissertation also delineates potential avenues to enhance MAGNNETO, particularly addressing challenges tied to current GNN architectures (e.g. over-smoothing and over-squashing). It envisions integrating Topological Deep Learning (TDL) techniques to foster a novel, promising approach to distributed optimization that has the potential to exploit arbitrary multi-element correlations, going beyond the traditional graph domain. By addressing the urgent need for efficient network traffic storage on networks with multiple vantage points, the proposed topological-inspired methodology reveals itself as a robust ML-based baseline for lossy data compression. In summation, this dissertation embarks on a pioneering journey to confront the elemental challenges of optimizing networked, graph-based systems. It unfurls the innovative MAGNNETO solution as a beacon of versatility and adaptability, displays its multifaceted applications, and heralds promising directions for future research, aiming to redefine the landscape of distributed network optimization and management in this digitally transformative era.