R4.2 In the Industry 4.0 era, Digital Twins (DTs) serve as virtual representations of physical objects and intermediaries between the physical world and the digital realm. DTs require proper modeling, design, and development to ensure their seamless integration along the cloud-to-edge continuum. In particular, this work introduces a microservices-based and serverless-ready model for DTs, laying the foundation for cost-effective DT deployment and orchestration. The joint adoption of microservices and serverless computing offers significant potential to address various challenges, including accommodating variable application requirements, managing load imbalances, and mitigating network faults. The proposed DT model has been implemented in different flavors: two serverless implementations—one that relies on a serverless framework of a cloud provider and one running at the edge on-premises—and a microservices one. These implementations have been experimentally evaluated with particular emphasis on the quality of cyber–physical entanglement. This work not only discusses the advantages and drawbacks of different implementations from a qualitative perspective but also quantitatively evaluates them with the in-the-field collection of experimental performance results. Notably, we report that a serverless implementation typically performs an order of magnitude worse than a microservices one in terms of entanglement, i.e., hundreds vs. tens of milliseconds.

Within the Industry 4.0 vision, digital twins (DTs) have gained great attention as a promising approach to improve remote monitoring and control by means of virtual representations of physical objects. However, while DTs are becoming more and more sophisticated and even adopted for mission-critical applications, their resilience assessment has not received the required consideration yet. This article originally proposes chaos engineering to assess and improve the resilience of DTs by testing multiple aspects of industrial environments in a coordinated, automated, and replicable manner. First, the article discusses why and how chaos engineering is promising to improve the resilience of DTs. Then, it identifies and introduces a set of chaos engineering profiles specifically designed to take into account the many aspects an industrial environment is composed of. Finally, it shows the feasibility of assessing the resilience of a proof-of-concept DT through a testbed based on widely-adopted, open-source tools.

The development of the Digital Twin (DT) approach is tilting research from initial approaches that aim at promoting early adoption to sophisticated attempts to develop, deploy, and maintain applications based on DTs. In this context, we propose a highly dynamic and distributed ecosystem where containerized DTs co-evolve with an orchestration middleware. DTs provide digitalized representations of the targeted physical systems, while the orchestration middleware monitors and re-configures the deployed DTs in light of application constraints, available resources, and the quality of cyber-physical entanglement. First, we lay out the reference scenario. Then, we discuss the limitations of current approaches and identify a set of requirements that shape both DTs and the orchestration middleware. Subsequently, we describe a blueprint architecture that meets those requirements. Finally, we report empirical evidence on both the feasibility and the effectiveness of a proof-of-concept implementation of the proposed ecosystem.

Digital Twins (DTs) have recently emerged as a valuable approach for modeling, monitoring, and controlling physical objects in Industrial Internet of Things applications. Measuring the quality of entanglement between the digital and physical counterparts plays a crucial role in the adoption of DTs. In this paper, we propose a concise yet expressive metric for representing the quality of entanglement, namely Overall Digital Twin Entanglement (ODTE), based on two key factors: timeliness and completeness. Furthermore, the paper presents the development of our industrial testbed implemented on top of Kubernetes, where we show practical applications of the proposed ODTE metric by highlighting and discussing its benefits in realistic use cases.

Traditional industrial networks were characterized by flat topologies, where industrial equipment exchanged a limited number of messages. In sharp contrast, modern manufacturing plants are evolving towards articulated environments generating an ever-increasing amount of network traffic. Such emerging environments prevent the adoption of traditional solutions based on end-to-end dispatching of few messages in reliable networks with bandwidth availability much greater than needed. This leads to the need to adopt proper message management strategies as close as possible to industrial equipment to avoid overwhelming the industrial network with non-mission-critical traffic at the expense of mission-critical one. This paper originally proposes edge-powered in-network processing to i) transparently manage messages sent by industrial equipment, ii) support a broad spectrum of message management strategies, ranging from efficient header-based solutions to expressive content-based ones, and iii) fulfill the application-dependent requirements demanded by industrial environments nowadays. Achieved performance results based on a proof-of-concept prototype demonstrate that the proposed solution efficiently provides content-based message management at the edge, even considering edge nodes with limited hardware capabilities.

The industrial internet of things has radically modified industrial environments, not only enabling novel services but also dramatically increasing the amount of generated traffic. Nowadays, a major concern within industrial plants is to support network-intensive services, such as real-time remote vibration monitoring of autonomous guided vehicles, while ensuring the prompt and reliable delivery of mission-critical safety-related messages among machines and the control room. To this purpose, we present a novel solution jointly orchestrating content-based message management and traffic flow steering: the former enables edge-powered in-network processing modules to process packet payloads as they traverse the industrial backbone, the latter supports dynamic (re)routing of traffic flows towards such processing modules. In particular, we exploit software-defined networking for flexible traffic flow (re)routing and Kubernetes for dynamic deployment on edge nodes of in-network processing modules for content-based message management. As demonstrated by performance results based on our working proof-of-concept prototype, our solution efficiently allows to manage industrial traffic flows in a coordinated fashion, by considering requirements of concurrently running industrial applications and the current state of the overall topology.

Industry 4.0 environments pose unique challenges for the realization of the communication substrate at the shop floor, due to the strict Quality of Service (QoS) requirements, the high heterogeneity of the employed data exchange protocols, and the different network technologies and addressing schema toward the machines. To address those issues, the paper proposes a distributed support based on a Message Oriented Middleware (MOM) and a Software Defined Network (SDN) control plane that coordinate to enable semantic routing by also allowing traffic differentiation as well as in-network processing at intermediate network nodes. Seminal results, collected in realistic industrial settings, confirm the feasibility of our proposal.