# A Virtualization Infrastructure Cost Model for 5G Network Slice Provisioning in a Smart Factory
## 4 Results and Discussion
### Cost Calculation and Breakdown  
Different redundancies can be assumed per slice type to calculate the number of servers required for the VNFs. A redundancy of one means there are no redundant components and that the minimum number of required components are deployed, while a redundancy of two means two components are deployed with one being redundant and so on. In this evaluation, a redundancy of one is assumed for eMBB and mIoT, while two is assumed for URLLC, V2X, and any other VNFs when deployed on common servers for different slice types. When calculating the number of servers based on required VMs, the required number of servers differ for the deployment scenario, as described in the cost model. The same server size and costs are utilized for all deployments for direct comparison. The additional infrastructure components such as required racks are based on the number of servers in each deployment type, and the number of switches is calculated as two per rack. Hence, the total CAPEX (racks, servers, switches), OPEX (maintenance, electricity for servers, switches, and electricity for cooling), and the TCO can be calculated. The costs are compared for the three scenarios, as shown in Figure 8. Scenario A (0.9 M) is significantly cheaper, followed by B (4.8 M), while C (9.6 M) is the most expensive in terms of TCO. While the scenarios differ significantly in total costs, the ratio of cost contributors in each of the scenarios is similar; the CAPEX consists of roughly 70% servers, followed by 20% switches, and 10% racks and cabling; the OPEX consists of roughly 42% maintenance, 37% Software and VNF licenses, and 21% electricity and cooling. This is because the servers and the supporting equipment are proportional to the number of customer devices, which in turn grow at the same rate in each scenario. Thus, the number of servers and supporting equipment required by the different deployment scenarios is different, but the ratio of their contribution to overall costs is similar. The breakdown enables different stakeholders to understand the contribution ratio of different components to overall costs. Furthermore, a comparison between the costs per slice type and the cost per slice type, per device type, for each scenario is also presented, as shown in Figures 9 and 10. Scenario A is the lowest cost deployment scenario, with all resources for multiple factory customers centralized in one cloud. Scenario B is next due to the distribution of UP resources to the factories with less centralization. Scenario C has the highest costs due to the absence of centralization and sharing of resources between the factories. Figure 7: VMs dimensioning for common CP VNFs. In each of the scenarios, the cost per slice type as well as per slice type, for each device can help stakeholders identify their respective investment requirements in the virtualization infrastructure and also help structure the fees from slice customers, which in turn are not necessarily the same. Figure 8: Cost comparison between deployment scenarios. Figure 10: Cost Comparison per slice type, per device type, for different deployment scenarios. Figure 9: Cost comparison between different slice types and deployment scenarios. turn affect future revenues. Since the VMs are not shared between the VNFs of different slices, and if a slice requires high performance and redundant equipment for high reliability but still consists of a very low number of devices such as the V2X slice, the TCO per slice type, for each device type will be significantly higher than that for other slices, with a higher number of devices, as can be seen in Figure 9. In the case of a very low number of devices, it is preferable to centralize the resources required by multiple slice customers in the same cloud, as in scenario A, in order to minimize costs, given that the performance requirements can still be met. The results show that scale benefits through centralization enable lower costs for scenario A, and the lack of scale benefits in scenario C leads to higher costs.