We are still in the midst of Industry 4.0 (I4.0), with more manufacturing lines being labeled as smart thanks to the integration of advanced ICT in Cyber–Physical Systems (CPS). While I4.0 aims to provision cognitive CPS systems, the nascent Industry 5.0 (I5.0) era goes a step beyond, aiming to build cross-border, sustainable, and circular value chains benefiting society as a whole. An enabler of this vision is the integration of data and AI in the industrial decision-making process, which does not exhibit yet a coordination between the Operation and Information Technology domains (OT/IT). This work proposes an architectural approach and an accompanying software prototype addressing the OT/IT convergence problem. The approach is based on a two-layered middleware solution, where each layer aims to better serve the specific differentiated requirements of the OT and IT layers. The proposal is validated in a real testbed, employing actual machine data, showing the capacity of the components to gracefully scale and serve increasing data volumes.

Kubernetes is a well-known orchestration and management solution for complex and large-scale service architectures in the Cloud Continuum. While it provides very valuable functions from the operation perspective, the high number of control loops it implements significantly enlarges the already wide space of configuration parameters and policies to consider for management purposes. We argue that optimizing complex Kubernetes deployments considering a multi-cloud and edge computing environment would significantly benefit from a Digital Twin approach, enabling an accurate virtual representation of a Kubernetes application to optimize its deployment and management policies. Towards that goal, this work illustrates the design of KubeTwin, a framework to implement Digital Twins of Kubernetes deployments. Furthermore, we present a validation of KubeTwin in a Multi-access Edge Computing (MEC) scenario, which shows its soundness in reenacting realistic Digital Twins of complex and highly distributed Kubernetes deployments. We believe that KubeTwin can provide useful guidance to the research community working in this field.

The Open Radio Access Network (O-RAN), manifested through the specifications established by the O-RAN Alliance, stands ready to transform the telecommunications ecosystem. In particular, the O-RAN Alliance reference architecture proposes hierarchical structures supported by several interfaces, including non-real-time and near-real-time functions. Non-real-time functions, denoted as rApps, encompass services, configurations, policy management, and RAN analytics. Conversely, near-real-time applications, denoted as xApps, serve as an open compute edge platform hosting applications from multiple vendors. Therefore, understanding the testing and deployment processes of rApps and xApps is pivotal for both researchers and practitioners. In this tutorial, we evaluate different 5G RAN deployments to determine the state of the art of available open-source solutions and assess their readiness for real cloud continuum environments by targeting solutions for simulation, emulation, and real testbeds. This tutorial empowers developers to gain a comprehensive understanding of O-RAN technologies, their development, and operations, enabling them to make well-informed choices for their target technologies. It considers both qualitative and quantitative benchmarks, as well as use cases, and facilitates a seamless transition of applications from simulated environments to real-world scenarios. Moreover, this tutorial provides a hands-on guide on setting up and migrating an xApp from a simulated environment to a real testbed. It includes a guide on deploying Service Management, Orchestration, and Non-Real-Time RAN Intelligent Controller and rApp. Finally, we identify existing open challenges and trace future directions to enhance the availability, portability, reliability, and configurability of 5G RAN deployments.

Software-defined networking decouples control and data plane in softwarized networks. This allows for centralized management of the network, but complete centralization of the controller functions raises potential issues related to failure, latency, and scalability. Distributed controller deployment is adopted to optimize scalability and latency problems. However, existing controllers are monolithic, resulting in code inefficiency for distributed deployment. Some seminal ongoing efforts have been proposed with the idea of disaggregating the SDN controller architecture into an assembly of various subsystems, each of which can be responsible for a certain controller task. These subsystems are typically implemented as microservices and deployed as virtual network functions, in particular as Docker Containers. This enables flexible deployment of controller functions. However, these proposals (e.g., $$\mu$$μ ONOS) are still in their early stage of design and development, so that a full decomposition of the SDN controller is not been available yet. To fill that gap, this article derives some important design guidelines to decompose an SDN controller into a set of microservices. Next, it also proposes a microservices-based decomposed controller architecture, foreseeing communications issues between the controller sub-functions. These design and performance considerations are also proven via the implementation of the proposed architecture as a solution, called Micro-Services based SDN controller (MSN), based on the Ryu SDN controller. Moreover, MSN includes different network communication protocols, such as gRPC, WebSocket, and REST-API. Finally, we show experimental results that highlight the robustness and latency of the system on a networking testbed. Collected results prove the main pros and cons of each network communication protocol and an evaluation of our proposal in terms of system resilience, scalability and latency.