Trustworthy artificial intelligence classification-based equivalent bandwidth control

Nowadays, machine learning (ML) is a viable solution for the allocation of equivalent bandwidth (EqB) in telecommunication networks, i.e. the minimum service rate required by a traffic buffer to guarantee a satisfactory Quality of Service (QoS). Moreover, trustworthy artificial intelligence (AI) is gaining importance in regulating the implementation of ML models, requiring explainable AI (XAI) and uncertainty handling. The paper extends prior works on the combined usage of control and rule-based classification for the EqB allocation, by adding the perspective of trustworthy AI. Simulation-based data collection is performed under a large setting of traffic conditions. Clopper-Pearson generalization bound is used as an efficient tool to select a rule-based model with adequate performance, also determining the minimum amount of data required for model training, which resulted in 3000 samples (~ 3.3 h of simulation). Also, robustness in terms of the model's capability to recognize out-of-distribution samples is studied, by comparing the different rates of satisfaction of rules in presence of training or operational data, which is quantified via mutual information, l1 and l2 norms. Results show that, while norms are more likely to capture the difference between training and operational data distribution, regardless its entity, mutual information seems sensitive to the entity of the separation between the training and the operational domains.