Machine Learning Based Resource Orchestration for 5G Network Slices

Abstract

5G will serve heterogeneous demands in terms of data-rate, reliability, latency, and efficiency. Mobile operators shall be able to serve all of these requirements using shared network infrastructure's resources. To this end, we propose in this paper a framework for resource orchestration for 5G network slices implementing four Quality of Service pillars. Starting from traffic classification, demands are marked so that they are best served by dedicated logical virtual networks called Network Slices (NSs). To optimally serve multiple NSs over the same physical network, we then implement a new dynamic slicing approach of network resources exploiting Machine Learning (ML). Indeed, as demands change dynamically, a mere recursive optimization leading to progressive convergence towards an optimum slice is not sufficient. Consequently, we need an initial well-informed slicing decision of physical resources from a total available resource pool. Moreover, we formalize both admission control and slice scheduler modules as Knapsack problems. Using our 5G experimental prototype based on OpenAirInterface (OAI), we generate a realistic dataset for evaluating ML based approaches as well as two baselines solutions (i.e. static slicing and uninformed random slicing-decisions). Simulation results show that using regression trees as an ML based approach for both classification and prediction, outperform other alternative solutions in terms of prediction accuracy and throughput.