# Cloud-Enabled Deployment of 5G Core Network with Analytics Features
## 4 The Testbed
### Slicing Configuration
The process presented in this section demonstrates that the proposed solution effectively resolves the issue discussed in Section 5.1 within the Open5 GS implementation of the 5G core. As Open5 GS is recognized as the most suitable open-source tool for deploying private 5G networks, especially in cloud environments, our contribution is anticipated to significantly bolster scientific efforts in this domain. ### Discussion on the Performance Evaluation

Our primary contribution lies in creating a robust environment designed for future research rather than solely focusing on benchmarking performance metrics. The proposed environment is expected to facilitate further research by supporting the testing, validation, and enhancement of different components and configurations of 5G networks, thus contributing significantly to the ongoing development of 5G technologies. In this subsection, we aim to provide insights into potential performance evaluations that can be conducted within our testbed environment. These evaluations are crucial for understanding how the 5G core network performs under various conditions and for ensuring it meets industry standards. We hope to guide researchers on how to leverage our testbed for comprehensive performance assessments, ultimately advancing the knowledge and capabilities of 5G network deployments. One of the key performance indicators for 5G networks is latency, which is expected to be at the 1 ms level in selected use cases. Measuring latency on a per-slice basis is crucial, and our testbed enables this through its slicing configuration. From the user's perspective, end-to-end latency is the most important metric as it directly affects service quality. This can be measured in the testbed using emulated UEs running common latency measurement tools such as iPerf. Network operators may also be interested in specific latency measurements for individual network functions, such as UPF in the data plane or AMF in the control plane. Our distributed environment allows for packet capturing at selected interfaces of different network functions, enabling precise latency measurements. Additionally, profiling tools such as the DPDK Test Suite or Linux perf tool can be utilized within selected network functions to gain deeper insights. Other critical performance indicators are throughput and scalability. The 5G core network should be capable of handling high data transfer rates and numerous concurrent connections. Our testbed's virtualized UEs can be effectively used for these purposes. Testing will involve generating high volumes of traffic and monitoring the network's ability to process this traffic efficiently and without losses. Well-known tools such as iPerf or JMeter are useful also in this context. Scalability concerns may also relate to the control plane capabilities, for instance, when a large number of UEs simultaneously register on the network, which could potentially lead to service degradation. The distributed architecture of the 5G core network should also be evaluated for its ability to support dynamic horizontal scaling by adding more instances of selected network functions. In addition to measurement and load generation tools, an orchestration layer should be introduced to manage these scaling operations. Although the primary aim of this work is not performance testing, we present some example results to provide a comprehensive verification of the testbed. In this study, we conducted tests using a single UE communicating through an isolated PDU session to ensure the results were unaffected by external factors. We performed eight consecutive measurements for each test and then conducted statistical analyses to calculate and present 95% confidence intervals. Figure 3 illustrates the end-to-end latency in the user plane of the 5G network. The observed latency performance meets expectations for 5G networks. However, it is important to note that the environment is fully virtualized, with virtual machines deployed on the same host.