Network Slice Embedding With Flexible Configurations in 5G Networks and Beyond

Network slicing enables the creation of multiple virtual networks (i.e., slices) over a shared network infrastructure, each tailored to a specific service. A key challenge lies in network slice embedding, which maps virtual network functions (VNFs) and links onto the physical network. Unlike prior works that assumed fixed configurations, we design a flexible system that allows for selecting the best configuration for each slice based on current physical resource availability during embedding. This leads to a joint optimization of (i) slice configuration selection (SCS) and (ii) slice admission control and embedding (SACE). To solve this, we propose two approaches: an exact method that formulates the joint SCS-SACE problem as an integer linear program (ILP), and a scalable alternative that decouples the problem, solving SCS via reinforcement learning and SACE via either ILP or a heuristic. Simulation results show that allowing flexible configuration selection improves slice acceptance by up to 10%, enabling more efficient slice deployment in resource-constrained networks.