Knowledge-based flexible resource allocation optimisation strategy for multi-tenant radio access network slicing in 5G and B5G

Abstract

Formalisation of the network slice as a resource allocation unit is considered a promising aspect that enables scalable and flexible resource allocation among many tenants in 5G and beyond 5G (B5G) communication networks. However, the user traffic has to be passed through the central administration for processing, which leads to latency problems. To solve this problem, recent research works have suggested fixed central-to-edge resource allocation ratios as per the service type. However, this approach leads to over-provisioning of some resources. This paper provides a flexible resource allocation approach for 5G slice networks operating in a heterogeneous environment with multiple tenants and tiers. A radial basis-neural network is used to convert abstract specifications of simulation activities into precise resource needs, and then a genetic algorithm-based flexible multi-resource allocation scheme is proposed, where a versatile optimisation framework is used. The results show that the proposed approach outperforms such existing schemes.