Link-level Blocking Prediction for Dynamic Network Slicing using Graph Neural Networks
Abstract
Network slicing enables the hosting of heterogeneous services by provisioning virtually isolated networks over a shared network infrastructure. Dynamic network slicing, which allows slices to scale resource usage at runtime, is a promising extension of the original static slicing concept. However, the unpredictable scheduling of resource scaling events can lead to potential blocking, where scale-up requests may be rejected due to resource contention. In this paper, we define a blocking prediction problem, where a slice provider assesses the risk of future blocking, given only the static specifications of slice requests. We propose a Graph Neural Network (GNN)-based solution that generalizes to arbitrary topologies and numbers of requests, predicting blocking events. Our simulation demonstrates that the proposed approach consistently outperforms a threshold-based method, offering a scalable and effective solution for dynamic network slice management.
