The development of 6G wireless technologies is rapidly advancing, with the 3rd Generation Partnership Project (3GPP) entering the pre-standardization phase and aiming to deliver the first specifications by 2028. This paper explores the OpenAirInterface (OAI) project, an open-source initiative that plays a crucial role in the evolution of 5G and future 6G networks. OAI provides a comprehensive implementation of 3GPP and O-RAN compliant networks, including Radio Access Network (RAN), Core Network (CN), and software-defined User Equipment (UE) components. This paper details the history and evolution of OAI, its licensing model, and the various projects under its umbrella, such as RAN, the CN, and the Operations, Administration and Maintenance (OAM) projects. It also highlights the development methodology, Continuous Integration/Continuous Delivery (CI/CD) processes, and end-to-end systems powered by OAI. Furthermore, the paper discusses the potential of OAI for 6G research, focusing on spectrum, reflective intelligent surfaces, and Artificial Intelligence (AI)/Machine Learning (ML) integration. The open-source approach of OAI is emphasized as essential for tackling the challenges of 6G, fostering community collaboration, and driving innovation in next-generation wireless technologies.

Edge computing is one of the key technology of the last decade, enabling several emerging services beyond 5G (e.g., autonomous driving, robotic networks, Augmented Reality (AR)) requiring high availability and low latency communications. While in cloud native paradigm, highly embraced by cloud providers, network functions and applications are decomposed into microservices run in a container. Defacto container orchestration engine, namely Kubernetes, deploys multiple containers inside a pod. The mapping between microservices and pod highly affects the availability and latency of deployed microservices and hence the run application. In this paper, we propose novel availability and latency-aware deployment models for an edge service composed of multiple applications designed as multiple microservices. The two considered deployments are analyzed and evaluated using experimentation and an analytical model, considering critical performance criteria for edge-oriented services, like availability and latency requirements.

Edge computing is one of the critical components enabling low-latency demanding services in beyond 5G networks. Indeed, the deployed applications at the edge benefit from their close position to end-users to guarantee low latency access. Considering the case of a network slicing enabled network, we introduce Lightweight edge Slice Orchestration (LeSO) frame-work, a cloud-native oriented orchestrator that orchestrates and manages the deployment of micro-services as sub-slices at the edge. Whilst the existing orchestration frameworks are greedy of computing resource consumption and fail to integrate with the Multi-access Edge Computing (MEC) domain, LeSO by design is very lightweight and integrates a MEC platform-like component to guarantee traffic steering to automate edge slice deployment. Experiment results show that LeSO necessities a small amount of CPU and memory, even when a high number of edge slices are deployed.

An efficient and scalable monitoring system is a critical component for any network to monitor and validate the functioning of the running services and the underlying infrastructure. This is more valid in 5G, as it relies on the network slicing concept, which adds many challenges to the monitoring system. Besides data isolation and multi-tenancy support, network slices require monitoring different types of resources, like RAN, computing, memory, network data rate, which belong to different technological domains, managed by different entities. Moreover, the monitoring system needs to be scalable, as in 5G a high-number of running network slices is envisioned. In this paper, we devise a novel monitoring framework for network slicing ready mobile networks, which features: 1) scalable monitoring system that supports a high number of running network slices in parallel; 2) technological domain agnostic thanks to a novel data collection (or monitoring) communication protocol; 3) support of multi-tenancy in a cloud-native environment. The framework has been implemented in a 5G facility, and its performance has been extensively evaluated.

Cloud-native technologies have recently entered the telecommunication world. These technologies were specially designed for developing and orchestrating container-based applications. The new cloud-native network functions use container based virtualization instead of virtual machine-based virtualization. These network functions have low resource footprints and low deployment time, making them suitable for a distributed environment. To adopt these new network functions and cloud native approach the network function virtualization vision needs alterations. In this paper, we use cloud-native approach to provide resilience to cloud-native network services. We proposed dynamic resource allocation and placement algorithm for modeling and placing a simple cloud-native network service. The algorithm aims to minimize infrastructural resource utilization under the constraint of abiding service availability mentioned in the service level agreement.

The Radio Network Information Service (RNIS) is one of the key services provided by a Multi-access Edge Computing Platform (MEP), as specified in the relevant ETSI MEC standards. It is responsible for interacting with the Radio Access Network (RAN), collecting RAN-level information about User Equipment (UE) and exposing it to mobile edge applications, which can in turn utilize it to dynamically adjust their behavior to optimally match the RAN conditions. Putting the provision of RNIS in the context of the emerging MEC-in-NFV environment, where the components and services of the MEC architecture, including the MEP itself, are integrated in an NFV environment and are delivered on top of a virtualized infrastructure, we present our standards-compliant RNIS implementation based on OpenAirInterface and study critical performance aspects for its provision as a virtual function. Since the RNIS design and operation follows the publish-subscribe model, we provide alternative implementations using different message brokering technologies (RabbitMQ and Apache Kafka), and compare their use and performance in an effort to evaluate their suitability for providing RNIS in an as-a-service manner.