Autonomous Resource Provisioning and Resource Customization for Mixed Traffics in Virtualized Radio Access Network

TLDR: Considering versatile users’ quality of service (QoS) requirements on transmission delay and rate, coarse resource provisioning scheme and deep reinforcement learning-based autonomous slicing refinement algorithm are proposed and a shape-based heuristic algorithm for user resource customization is devised to improve resource utilization and QoS satisfaction.

Abstract: Network slicing has been introduced in fifth-generation (5G) systems to satisfy requirements of diverse applications from various service providers operating on a common shared infrastructure. However, heterogeneous characteristics of slices have not been widely explored. In this paper, we investigate dynamic network slicing strategies with mixed traffics in virtualized radio access network (RAN). Considering versatile users’ quality of service (QoS) requirements on transmission delay and rate, coarse resource provisioning scheme and deep reinforcement learning-based autonomous slicing refinement algorithm are proposed. Then, a shape-based heuristic algorithm for user resource customization is devised to improve resource utilization and QoS satisfaction. In principle, the DQN algorithm allocates only the necessary resource to slices to satisfy users’ QoS requirements. For fairness in comparison, we reserve all the unused resources back to the slices. In case there is a sudden change in user population in one slice, the algorithm provides isolation. To validate the advantage, system-level simulations are conducted. The results show that the proposed algorithm balances the satisfaction up to about 100% and resource utilization up to 80% against state-of-the-art solutions. The proposed algorithm also improves the performance of slices in mixed traffics against state-of-the-art benchmarks, which fail to balance satisfaction and resource utilization in some slices.