  Next generation wireless networks are expected to support diverse vertical
industries and offer countless emerging use cases. To satisfy stringent
requirements of diversified services, network slicing is developed, which
enables service-oriented resource allocation by tailoring the infrastructure
network into multiple logical networks. However, there are still some
challenges in cross-domain multi-dimensional resource management for end-to-end
(E2E) slices under the dynamic and uncertain environment. Trading off the
revenue and cost of resource allocation while guaranteeing service quality is
significant to tenants. Therefore, this article introduces a hierarchical
resource management framework, utilizing deep reinforcement learning in
admission control of resource requests from different tenants and resource
adjustment within admitted slices for each tenant. Particularly, we first
discuss the challenges in customized resource management of 6G. Second, the
motivation and background are presented to explain why artificial intelligence
(AI) is applied in resource customization of multi-tenant slicing. Third, E2E
resource management is decomposed into two problems, multi-dimensional resource
allocation decision based on slice-level feedback and real-time slice adaption
aimed at avoiding service quality degradation. Simulation results demonstrate
the effectiveness of AI-based customized slicing. Finally, several significant
challenges that need to be addressed in practical implementation are
investigated.
