## 5 Conclusions  
We have focused on latency-aware DU and CU placement (LDCP) in packet-based NGFI networks. The LDCP optimization problem was modeled as a mixed-integer linear programming problem. We have made use of the latency model that estimates worst-case latencies of flows to guarantee that the traffic flows carried over the packet-switched network satisfy latency constraints. To evaluate performance of the LDCP-MILP model and NGFI network, we have considered different network scenarios varying in topologies, the number of RUs, PP capacities, and link lengths were considered.  
Solving the LDCP-MILP model is feasible for network instances consisting of some tens of RUs and about 20 switching nodes. Indeed, for the network scenarios considered in this work, we have obtained good-quality solutions when solving the model. At the same time, for larger problem instances we have encountered the issue of lack of memory during solving the model by a commercial mixed-integer programming solver (CPLEX). Note that introduction of additional constraints into the MILP model, such as routing constraints, will make the problem more complex. Therefore, optimization of larger networks and extended network scenarios will require efficient heuristics and/or the application of advanced MILP optimization techniques, such as column generation and cut generation. In our previous works, we have shown the effectiveness of hybrid optimization algorithms--combining different processing and optimization techniques--in solving large problem instances that are difficult to be treated by mathematical integer programming solvers [37]. We plan to develop such optimization methods in our future work in the context of packet-based NGFI networks.  
As we have shown in the analysis of network performance, a higher number of active PP nodes is required in larger networks to keep flow latencies within allowable limits. At the same time, the number of active PPs decreases if more capacity is available in the PP nodes. In general, the latency of the overall (RU-DU-CU-DC) flow does not change significantly if more PP capacity is available. It comes from two opposite effects that compensate each other, namely the decrease of the number of active PP nodes and the increase in joint DU/CU processing. In particular, the former effect may increase the BH flow latencies, while the latter decreases the total latency of MH flows. Eventually, we have observed that proper selection of the PP node capacity may lead to minimization of the number of active PP nodes without a significant overhead in the total PP capacity deployed in the network. This in turn results in minimization of the network cost.  
In future works, we will focus on different problems that exist in NGFI networks and that require dedicated optimization algorithms, including network slicing or network survivability. We will also consider more diversified network scenarios, including the networks in which some of the links are wireless links. Finally, we plan to work on improving MILP formulations and to make use of  
Figure 9: (**a**) Percentage of demands with DU and CU processing performed in the same PP node (left) and (**b**) average usage of PP capacity of active PP nodes (right) in a function of PP capacity multiplier (\(C\)) in networks DRING-16 and MESH-20.  
advanced optimization methods, with the aim to develop optimization methods applicable to larger network scenarios.  
This research was funded by National Science Centre, Poland, under grant number 2018/31/B/ST7/03456.  
The author declares no conflict of interest.