Nelson Luis Saldanha da Fonseca

arXiv2025

eXplainable Artificial Intelligence for RL-based Networking Solutions

Reinforcement Learning (RL) agents have been widely used to improve networking tasks. However, understanding the decisions made by these agents is essential for their broader adoption in networking and network management. To address this, we introduce eXplaNet - a pipeline grounded in explainable artificial intelligence - designed to help networking researchers and practitioners gain deeper insights into the decision-making processes of RL-based solutions. We demonstrate how eXplaNet can be applied to refine a routing solution powered by a Q-learning agent, specifically by improving its reward function. In addition, we discuss the opportunities and challenges of incorporating explainability into RL to better optimize network performance.

cs.NI↗

arXiv2024

Data-driven Intra-Autonomous Systems Graph Generator

Accurate modeling of realistic network topologies is essential for evaluating novel Internet solutions. Current topology generators, notably scale-free-based models, fail to capture multiple properties of intra-AS topologies. While scale-free networks encode node-degree distribution, they overlook crucial graph properties like betweenness, clustering, and assortativity. The limitations of existing generators pose challenges for training and evaluating deep learning models in communication networks, emphasizing the need for advanced topology generators encompassing diverse Internet topology characteristics. This paper introduces a novel deep-learning-based generator of synthetic graphs representing intra-autonomous in the Internet, named Deep-Generative Graphs for the Internet (DGGI). It also presents a novel massive dataset of real intra-AS graphs extracted from the project ITDK, called IGraphs. It is shown that DGGI creates synthetic graphs that accurately reproduce the properties of centrality, clustering, assortativity, and node degree. The DGGI generator overperforms existing Internet topology generators. On average, DGGI improves the MMD metric $84.4\%$, $95.1\%$, $97.9\%$, and $94.7\%$ for assortativity, betweenness, clustering, and node degree, respectively.

cs.NI↗