Using Counterfactuals to Proactively Solve Service Level Agreement Violations in 5G Networks

Abstract

A main challenge of using 5G network slices is to meet all the quality of service requirements of the slices (which are agreed with the customer in a service level agreement (SLA)), throughout the network slices' lifecycle. To avoid the penalty for violation, a proactive solution is presented, including predicting the SLA violation, explaining the violation cause, and then providing an adaptation to traffic. This work uses counterfactual (CF) explanations to 1) explain the main factors affecting the identified model's SLA violation prediction and 2) present modifications in the input values, which are required to configure the network traffic to avoid such a violation. We evaluate the CF explanation at two different levels where the generated CF instance is fed to the predictive model, and then actuation data are generated to evaluate the result in the real network. Our solution minimizes the violation up to 98%. This information can be utilized to reconfigure the system, either by humans or by the system automatically, to make it fully autonomous on the one hand and comply with the 'right to explanation' introduced by the European Union's General Data Protection Regulation on the other hand.