# A Novel Framework for Cross-Cluster Scaling in Cloud-Native 5G NextGen Core
## 1 Introduction  
Network slicing architecture has emerged in 5G as a key feature to efficiently support dynamic resource allocation in a multi-tenant environment, as to offers solutions for isolating the network for various industries and use cases. In this manner, the mobile network operators (MNOs) can configure and manage the control plane and user plane network functions along with the corresponding resources (e.g., access, transport, and core networks) to support various slice/service types (SST). A network slice can be defined as a logical end-to-end network that is dynamically created, and a user may access multiple slices over the same gNB. Each slice may serve a particular service type with an agreed-upon service-level agreement (SLA) [1]. In the technical specification, TS 23.501 v16.4.0 [2], the 3 rd generation partnership project (3 GPP) provides a standardized classification to classify different services: enhanced mobile broadband (eMBB) services, ultra-reliable low-latency communications (URLLC) services, massive internet of things (IoT) services (MIoT), and vehicular-to-everything communications (vehicles, infrastructure, pedestrians, etc.). Network slicing is mostly tackled in the existing solutions through network virtualization, which is encompassed by the NFV MANO (network functions virtualization management and orchestration)framework to manage and orchestrate VNFs (virtualized network functions). An important pillar addressed by NFV MANO is service availability and systems capacity to deal with failover, whereas the operation support system (OSS) and business support system (BSS) do not fulfill the recovery and failover role for network slicing. Moreover, the existing NFV MANO systems such as ONAP [3] and OSM [4] are dedicated to onboarding and instantiating VNFs, but regarding application runtime orchestrations, it has been observed that they lack the ability to scale operations properly [5]. Especially due to the increase in traffic reflected on both the user and control plane, the scalability becomes a virtualization deployment challenge [6; 7]. Another challenge that network slicing imposes in terms of intensive computational power is the efficient management and utilization of the resources [8; 9]. Most of the deployments are limited in terms of physical or virtual resources, whereas network slicing for different verticals requires consistent amounts of networking, computing, or storage resources to be allocated on demand. Security is a mandatory requirement when it comes to the operational aspect of the telco network, therefore any open-source implementation of the mobile stack needs to adhere to the security standards in terms of multi-site deployments [10]. Service monitoring is also a recurrent concern in the multi-layered architecture of 5G, which might require data aggregation from multiple sources to be able to ensure SLA monitoring at each level of the mobile stack [11; 12]. The GSMA [13] paper handles the end-to-end network slicing through network automation, which is an important requirement in supporting the deployment of slices through APIs provided by a network slice provider. The network automation technology is expected to roll out network slices and modify them in an agile and automated fashion. In contrast to the limited capabilities of virtualization for scaling of network slicing, the container orchestration, specifically Kubernetes standalone, which is the de facto standard, leverages automation for cloud-native life cycle management such as automatic scaling, scheduling, and self-healing independently of the lack of resources at the edge of the network [14]. In contrast to the classical cloud applications, telco applications are geographically distributed across multiple data centers. The main challenge in cross-location deployments is the service performance, especially for delay-sensitive services, which might experience delay, jitter, or packet loss.