DRL-based customised resource allocation for sub-slices in 6G network slicing

Abstract

6G network services demand significant computer resources. Network slicing offers a potential solution by enabling customized services on shared infrastructure. However, dynamic service needs in heterogeneous environments pose challenges to resource provisioning. 6G applications like extended reality and connected vehicles require service differentiation for optimal quality of experience (QoE). Granular resource allocation within slices is a complex issue. To address the complexity of QoE services in dynamic slicing, a deep reinforcement learning (DRL) approach called customized sub-slicing is proposed. This approach involves splitting access, transport, and core slices into sub-slices to handle service differentiation among 6G applications. The focus is on creating sub-slices and dynamically scaling slices for intelligent resource allocation and reallocation based on QoS requirements for each sub-slice. The problem is formulated as an integer linear programming (ILP) optimization problem with real-world constraints. To effectively allocate sub-slices and dynamically scale resources, the Advantage Actor-Critic (A2C)-based Network Sub-slice Allocation and Optimization (NS-AO) algorithm is proposed. Experimental results demonstrate that the proposed algorithm outperforms the state of the art in terms of training stability, learning time, sub-slice acceptance rate, and resilience to topology changes.