The ongoing digitization of Operational Technology (OT) and the integration of Industrial Internet of Things (IIoT) devices are fundamentally transforming modern industrial systems. However, the heterogeneity of hardware and software ecosystems amplifies the complexity of this transformation, making orchestration and lifecycle management especially challenging. To address these challenges, modern DevOps practices—such as Continuous Integration and Continuous Deployment (CI/CD), runtime observability, and fine-grained access control—are essential. These practices help accelerate time-to-market, maintain service quality, manage operational complexity, and uphold security standards. Advancing toward seamless integration of OT within cloud-native environments, we introduce a prototype Orchestrator based on the Kubernetes plarform, aimed at providing a comprehensive lifecycle management for OT devices in industrial contexts. Our proposal bridges the traditional divide between IT and OT by seamlessly integrating with existing CI/CD pipelines and implementing Role-Based Access Control (RBAC) tailored to device-level permissions. Through a unified abstraction layer, it simplifies the management of heterogeneous, multi-vendor hardware, enabling scalable, secure, and efficient operations across diverse industrial deployments.

The Open RAN specification introduces the Near Real-Time RAN Intelligent Controller (Near-RT RIC) as a closer point of orchestration, providing real-time analysis and control of gNBs through Operator-defined Near-Real Time Applications (xApps). The continuous gathering, storing, and processing of metrics and logs from various hardware and software components of the network is a complex task. In the cloud community, this complexity has been addressed through approaches and tools under the umbrella of observability. An observation framework applied to O-RAN can assure a holistic view of the infrastructure and services running on it, helping optimize performance, ensure interoperability among multi-vendor systems, enhance scalability, bolster security, and reduce operational costs by providing realtime insights. However, existing observability frameworks poorly fit O-RAN use cases due to their service-oriented architecture, which impacts performance and scalability. To fill this gap, we propose CLO5ER, a framework that facilitates the creation of observability workflows through the composition of O-xApp. Our solution integrates seamlessly with existing observability frameworks, providing an accelerated alternative path for processing signals from gNBs. Additionally, we introduce a novel xApp controller that optimizes O-xApp placement and instrumentation. Furthermore, our solution features a hierarchical message-oriented middleware that enhances xApp composability and data exchange.

Open Cloud (O-Cloud) is the Open Radio Access Network (O-RAN) computing infrastructure spanning edge to cloud sites defined by the O-RAN Alliance to support and coordinate RAN infrastructure management shared among multiple Mobile Network Operators (MNO). In the standard definition, O-Cloud ensures reliable connectivity, active coordination, and efficient distribution of the Radio Access Network (RAN) deployments. Thanks to these properties, O-Cloud can support Mobile Network and Infrastructure operators to achieve fully automated infrastructure management, self-service MNO portals, enhanced security measures, and O-Cloud infrastructure-agnostic management. In this paper, we present TORNADO: TOSCA-enabled Orchestration for RAN in Next-Generation Networks Automating DevOps in O-Cloud, an O-Cloud automation infrastructure designed to ease DevOps deployment and operation phases of RAN network functions for multiple MNOs. Our solution introduces infrastructure-agnostic automation for multi-site, multi-MNO RAN components, offering high-level, secure self-service MNO portals for defining RAN deployments. Performance evaluation of our solution for various RAN network functions demonstrated the capability of TORNADO to automate the deployment in a multi-site, multi-MNO heterogeneous infrastructure.

The increasing reliance on Machine Learning (ML) to extract insights and value from data is driving researchers and businesses to seamlessly integrate it throughout the entire Cloud Continuum (CC), spanning from large-scale cloud infrastructures to edge computing and end devices. However, traditional service orchestration frameworks are fine-tuned to manage single-site general-purpose applications, struggling with the complexity of CC ML deployments, which seek customized strategies for enhanced performance and resiliency. To this end, we propose Machine Learning cloud Continuum Operations (MLCOps), an overlay solution that supports ML applications in CC deployments. MLCOps implements an innovative multisite orchestration layer and provides a new service runtime support, allowing one to react quickly to changing conditions. MLCOps advocates a layered approach to carry out specialized strategies, implementing global and local actions on infrastructure and environment to ensure the performance and availability of the deployed ML services. Without loss of generality, we show how the framework could be instantiated to support an anomaly detection task. In this context, we assess various (re)configuration strategies that can take place in actual deployments, assessing the ability of MLCOps to preserve the reliability of CC ML application deployments under dynamic workload conditions.

The paradigm shift towards the fifth generation of mobile networks (5G) has entailed a transition from a monolithic architecture to a completely disaggregated microservice-based architecture of network functions. This architectural renovation decouples and distributes Containerized Network Functions (CNF), tailored to address the specialized requirements of the Open Radio Access Network (Open RAN) deployment. Specifically, Kubernetes has emerged as a pivotal orchestrator for CNF within the context of disaggregated Open RAN. In particular, the deployment of Operator-defined Applications (xApps) for Near Real-Time RAN Intelligent Controller (Near-RT RIC) scenarios calls for communication substrates to facilitate the communication and coordination with other RAN components, and consequently, to dial with increased dynamicity and flexibility of 5G deployments. To face these issues, we propose service mesh as a cloud-native technology extending the Kubernetes communication infrastructure. Service mesh fulfills diverse requisites of xApps running in the RAN Intelligent Controller for traffic management, instrumentation, security, and observability, inherently facilitating a zero-trust architecture through the incorporation of sidecar proxies co-located with microservices. Moreover, we evaluate the performances of another emerging paradigm, raising as a lightweight solution that allows a more incremental adoption compared to service mesh, represented by ambient mesh. This paper analyses service and ambient mesh for deploying xApps in a state-of-the-art Near-RT RIC, i.e., the O-RAN SC implementation, evaluating both qualitative and quantitative attributes.

The advent of the Open Radio Access Network (O-RAN) specifications for 5G and 6G Radio Access Networks (RANs) has brought forth a great interest in the use of machine learning to perform control and management tasks. The integration of machine learning in the O-RAN architecture is initially envisioned to be implemented through xApps, applications that act in a near-real timescale and that have machine learning models meant for specific tasks. However, the development of machine learning-based xApps presents challenges, as although the xApp architecture facilitates component reusability for the RAN, the state-of-the-art architectures for xApps themselves require the implementation of an ad-hoc xApp for each machine learning model. Therefore, these architectures limit the reusability of the components of xApps as applications, even for xApps meant for the same purpose. To address these issues, we propose the Intelligent xApp Architecture (IxAA), a software architecture to simplify the implementation of machine learning-based xApps with a focus on reuse, easing the comparison of machine learning models. As a proof of concept, we developed xAssessment, an xApp to evaluate the performance of data prediction models. Our evaluation shows the performance results of five machine learning models predicting three different RAN metrics through xAssessment in a simulated O-RAN testbed.

Effective communication and information sharing among different districts and cities are crucial for the management of utility flows, traffic, and emergencies in smart cities. In this scenario, a smart city requires cloud-native solutions to collect and analyze data from various sources, including traffic sensors and public transport vehicles. Thus, a multicloud observability approach is proposed to aggregate data from different localities. The solution aims to provide a complete suite for observability capable of collecting data across layers of a multicloud and integrating already existing open-source projects.