## 1 Introduction
Network slicing brings unique open problems and algorithmic challenges arising, among others, from the provisioning of processing and transmission resources for individual slices for which slice isolation and heterogeneous, slice-specific QoS guarantees should be ensured in shared network infrastructure [17]. QoS provisioning is challenging in packet-switched Xhaul networks because the nondeterministic packet transmission causes unpredictable buffering delays in network switches. This issue is especially relevant in the case of latency-sensitive services, such as URLLC, which may require a special treatment of their packet flows compared, for instance, to the flows related to eMBB services. At the same time, the deployment of network slices should be optimized regarding the usage of processing and transmission resources to minimize the network cost. For this purpose, dedicated optimization methods are necessary. MILP is a frequently used method for modeling and solving optimization problems in communication networks [18]. These problems are generally \(\mathcal{NP}\)-hard, indicating that no known algorithms can deterministically solve them within polynomial time. In practice, solving MILP models can be challenging and time-consuming, especially for problems involving a large set of integer variables. Enhancing the quality of MILP formulations and applying advanced MILP-based optimization techniques can significantly facilitate solving optimization algorithms. CG is one technique that reduces the complexity of solving MILP models [19]. In CG, this is achieved by initializing the MILP model with a small, basic set of problem variables and the iterative, dynamic addition of selected variables (columns) to the model, improving the optimization objective value. This work studies this approach in the network slicing problem. This work focuses on optimizing network slices' planning in packet-switched Xhaul networks assuming specific slice requirements. In the network-planning approach, processing resources are assigned to network slices, and connections are established over a packet Xhaul transport network, assuming given Service Level Agreements (SLAs) regarding traffic loads supported by the slices and traffic flow latency requirements. Such an approach reduces the impact of the uncertainty in traffic flows on the network performance while ensuring a stable network operation if the maximum traffic loads supported by network slices are considered during network planning [20]. If the network slices are planned for lower than the maximum volumes of supported traffic, additional network mechanisms should be applied to ensure a reliable network operation and the fulfillment of the slice's SLAs. The development of such mechanisms is left for future work. Our previous studies concerned the optimization of latency-aware packet-switched Xhaul networks, where the problem of DU and CU placement was addressed jointly with the routing of Xhaul packet flows for a single network slice case [20; 21; 22]. This study fulfills the gap in previous works by accounting for network slicing in the packet Xhaul network scenario. First, the optimization model formulated for the network-planning problem extends previous MILP models by introducing relevant network slicing-related constraints. Second, as the MILP model has limited scalability, a new optimization algorithm using the CG approach is developed. The novelty and main contributions of this study are summarized below:  
* A network slicing-planning problem is addressed in the packet-switched Xhaul transport network with soft slice isolation and buffering latencies considered. * The above problem is formulated as an optimization problem modeled using the MILP approach and assuming specific DU/CU placement and latency requirements of eMBB and URLLC network slices. * The effective optimization method PBA based on the CG optimization technique is developed to facilitate solving the MILP problem for larger network instances.