# A Novel Framework for Cross-Cluster Scaling in Cloud-Native 5G NextGen Core
## 1 Introduction
Nevertheless, a drawback of the current cloud-native solutions for 5G mobile core deployment is that they are centered around Kubernetes standalone clusters and lack a broader approach for multi-clusters and multi-cloud scenarios in terms of automation of network slicing. Unlike other standalone Kubernetes deployments, this paper addresses the Kubernetes full-stack capabilities in terms of multi-cluster scalability for a multi-site 5G deployment by introducing a new framework to scale both horizontally and vertically the resources shared across isolated Kubernetes clusters. Moreover, due to the Kubernetes APIs that work over the internet, the connectivity can be extended to different cloud providers as well as to on-premises sites. This emphasizes the idea of unlimited and on-demand utilization of resources for a 5G mobile core instance running as cloud-native. We consider our contributions fourfold below:  
* Our main contribution depicted in Figure 1 covers all layers of the MANO framework since it addresses the multiple concerns in terms of self-management, scheduling, scalability, end-to-end service monitoring, and foremost the multi-site deployment performance challenge. We also compare the two frameworks and map the correspondence between the functional blocks. Another important aspect of this approach is the network isolation provided by the cloud ecosystem, which uses the concept of tenants running in separate data centers. In our proposed design, we benefit from the Kubernetes capabilities in terms of operators, which are software extensions; in our case, the Liqo operator [15] runs inside the cluster. Moreover, by peering multiple Kubernetes clusters using Liqo, we ensure unlimited capacity and resources for the scalability and deployment of mobile core, which unblocks the barriers for network slicing and on-demand resource consumption. * A secondary contribution concerns the comparison between different mechanisms for Liqo offloading. In our setup, we use Liqo to peer the clusters and employ the Kuelet [16] component, which is part of the Kubernetes architecture to operate at the level of eachb node. In this manner, we can offload the workload between clusters that reside in different tenants as well as separate data centers and regions in the public cloud. We test and analyze three scenarios for offloading the control and user planes of the 5G mobile core. To demonstrate the flexibility of the cloud-native approach, we run the Open5 GS [17], which is an open-source simulator for virtualized 5G mobile cores that we run in containers orchestrated by Kubernetes. * The third contribution consists of the system's performance validation, which is achieved by configuring and deploying end-to-end network slicing corresponding to three verticals, which is analyzed in terms of latency and throughput for the proposed offloading scenarios. In order to configure the slices, we use _UERANSIM_[18], which is an emulator for the RAN (radio access network). * The fourth contribution looks at the telemetry solutions; hence, we endow our setup with the monitoring capabilities for inter-service communication by employing Istio [19] specific to service-mesh topologies. Furthermore, this paper is structured as follows: Section 2 gives an overview of the existing body of literature for existing ETSI MANO (network functions virtualization management and orchestration) open-source projects as well as cloud-native testbeds that validate end-to-end network slicing scenarios. Additionally, in Section 3, we provide a thorough correlation between the MANO framework and Kubernetes orchestration, assessing scalability for multi-cluster and multi-cloud deployments. In Section 4, we present our setup and configuration for the three proposed offloading scenarios of the 5G user and control planes. The following two Sections, Sections 5 and 6, reveal an in-depth view of the functionality of the testing environment as well as highlight the differences between out-of-band and in-band peering in terms of configuration and deployment model.