Drift-Aware Policy Selection for Slice Admission Control

Fifth-generation (5G) mobile networks are expected to support the dynamic provisioning of services with heterogeneous Quality of Service requirements through Network Slicing. However, the uncertainty in the resource requirements of the tenant’s future Network Slice Requests raises the problem of how Network Slices can be admitted onto the mobile network infrastructure. To address this, we investigate the Slice Admission Control problem in virtualization-enabled mobile networks. Specifically, we focus on the scenario in which a controller needs to select an Admission Control policy (or algorithm) based on the patterns of previous Network Slice Requests and formulate such a problem as a Multi-Armed Bandit problem. By leveraging Online Learning (OL), we propose a framework, Drift-AwaRe upper confIdence bOund (DARIO), that adaptively selects and learns the performance of online Slice Admission Control (SAC) policies by monitoring for changes in the underlying patterns of Network Slice Request (NSR) features. We evaluate the performance of our framework in terms of the relative gains in average revenue, acceptance ratio, and average resource utilization when compared to both static and adaptive baselines and show that we outperform the considered baselines for the considered metrics.