Neural Network-based Blocking Prediction for Dynamic Network Slicing

Network slicing in Optical Transport Networks (OTNs) is a promising technology to provide hard resource isolation and performance assurance of services with diverse requirements. When network slices are allowed to request their scaling based on their service traffic trends (i.e., dynamic network slicing), it is difficult for a slice provider to maintain resource isolation and high resource utilization simultaneously. We formulate a blocking prediction problem for the slice provider to assess the possibility of future blocking events for a given set of slice requests. We use a Multi-Layer Perceptron classifier to identify a set of slice requests that would cause blocking. It is demonstrated by the simulations with different request patterns that our proposal outperforms other learning-based approaches. The results indicate that a slice provider could assess the risk of experiencing a violation of service assurance when accepting a set of requests and take an appropriate countermeasure such as admission control, using our prediction module.