  In this paper, we design a resource management scheme to support stateful
applications, which will be prevalent in 6G networks. Different from stateless
applications, stateful applications require context data while executing
computing tasks from user terminals (UTs). Using a multi-tier computing
paradigm with servers deployed at the core network, gateways, and base stations
to support stateful applications, we aim to optimize long-term resource
reservation by jointly minimizing the usage of computing, storage, and
communication resources and the cost from reconfiguring resource reservation.
The coupling among different resources and the impact of UT mobility create
challenges in resource management. To address the challenges, we develop
digital twin (DT) empowered network planning with two elements, i.e.,
multi-resource reservation and resource reservation reconfiguration. First, DTs
are designed for collecting UT status data, based on which UTs are grouped
according to their mobility patterns. Second, an algorithm is proposed to
customize resource reservation for different groups to satisfy their different
resource demands. Last, a Meta-learning-based approach is developed to
reconfigure resource reservation for balancing the network resource usage and
the reconfiguration cost. Simulation results demonstrate that the proposed
DT-empowered network planning outperforms benchmark frameworks by using less
resources and incurring lower reconfiguration costs.
