# Cloud-Enabled Deployment of 5G Core Network with Analytics Features
## 4 The Testbed
### Slicing Configuration
## 7 Conclusions

In this work, we presented a testbed for the virtualized cloud-based deployment of the 5G network, designed to facilitate slicing, a configuration that can be particularly challenging. To aid similar deployments, we provide detailed information on our configuration process. We conducted an initial verification of the testbed, identified an error-prone scenario, and proposed an original solution to support similar cloud-based deployments. Additionally, we extended the existing implementation with a new network function, termed quasi-NEF, which serves as an interface to the UDR. This module can be seamlessly integrated into cloud-based deployments and enhances functionality with advanced analytics, a crucial feature in complex, virtualized environments. Specifically, the quasi-NEF aggregates 5G-related data, offering valuable insights for mobile network operators and 5G slice tenants. Configuration files, source code, listings, and additional figures are available in [42]. For future work, we plan to conduct comprehensive performance tests on the prepared deployment, verifying the migration process to the public cloud and comparing the performance of local versus cloud installations. While our current work provides a robust foundation, there are limitations in our performance testing that we acknowledge and plan to address in future work. Key performance indicators such as latency, throughput, and scalability will be measured using our testbed. Latency will be assessed on a per-slice basis, as enabled in the testbed. Throughput and scalability will be also evaluated by generating high volumes of traffic with virtualized UEs and monitoring the network's ability to handle this traffic efficiently. We will also assess the control plane's capability to manage large numbers of simultaneous UE registrations and the network's ability to support dynamic horizontal scaling with an orchestration layer. In addition to performance evaluations, we aim to develop and automate our solution, focusing on dynamic network function management. Further efforts will explore container-based deployments in comparison to the existing virtual machine-based approach. As we move forward, we will explore container-based deployments to assess their impact on performance and scalability. This will involve adapting our testbed to support container orchestration platforms such as Kubernetes, which will enable more efficient resource management and scalability. It will also be valuable to verify the performance of the testbed deployed using server-level virtualization. Through these future works, we hope to provide a robust and flexible testbed environment that advances the knowledge and capabilities of 5G network deployments. Recognizing the limitations of the 5 GCN software used in the testbed, we designed and implemented the quasi-NEF as an intermediary step towards a full-fledged NEF. The quasi-NEF could be enhanced to support event exposure by enabling external applications to subscribe to 5G network events, such as UE deregistration. The full NEF would allow external applications to receive notifications of specific network events by subscribing to the relevant network functions. Potential future work regarding the NEF function includes conducting comprehensive evaluations to measure its impact on network efficiency and analytics capabilities. Further research could explore integrating advanced machine learning algorithms to enhance data processing and predictive analytics within the NEF framework. Finally, investigating the scalability of the NEF in larger, real-world 5G deployments can provide insights into its practical applications and limitations. Conceptualization, M.Z. and P.B. ; methodology, M.Z. and P.B. ; software, M.Z. ; validation, M.Z. and P.B. ; investigation, M.Z. ; data curation, M.Z. ; writing--original draft preparation, M.Z. and P.B. ; writing--review and editing, P.B. and M.N. ; visualization, P.B. and M.N. ; supervision, P.B. and M.N. ; project administration, P.B.