## 2 Background
### Related Work  
The authors in [6] present an extensive overview of edge computing by positioning edge computing and discussing the state of the art related to the edge computing paradigm, characteristics (e.g., big data analytics, resource management, security, and  
Figure 1: Edge and cloud scenarios with Kubernetes. privacy), and requirements (e.g., resource management, real-time application support, scalability) in detail. The paper ends with a summary of open challenges for edge computing, emphasizing service discovery and mobility, robust deployment models, and collaboration between heterogeneous edge computing systems. In [17], the authors show the multi-access edge computing framework and reference architecture, present an extensive survey about the MEC integration and deployment options into 5G networks, different MEC enablers (e.g., network function virtualization, network slicing), and overview some MEC approaches for latency, compute resources, and energy efficiency optimization in the MEC concept. In [18], the authors propose a generic, hierarchical, layered architecture for smart manufacturing based on edge, fog, and traditional cloud computing and discuss which manufacturing processes can be handled by a certain layer considering the industry use case requirements. The paper also presents an illustrative application example. The authors in [19] propose a hybrid computing framework considering the device, cloud, and edge compute capabilities, as well as design and evaluate an intelligent resource scheduling strategy to fulfill the real-time requirement in smart manufacturing. In [20], the authors present a reference architecture and offer various practical approaches for consideration to embark on the digital journey towards smart manufacturing, as well as analyze the pros and cons of the different approaches. In [21], a system architecture is described for an IoT-based manufacturing scenario, covering device, network, data, and application domain. A case study is also presented for active maintenance where the authors build a distributed data processing system in the cloud. In [22], the use of edge computing in automation and manufacturing industrial contexts is presented, describing the problem space and introducing a reference architecture model for edge computing. In [23], the authors give an overview of the existing solutions of edge computing in 5G, and a taxonomy is given in which the edge computing approaches are classified, also showing that the convergence of edge computing and 5G brings new issues to be resolved. In [24], the authors briefly summarize the different NPN deployment modes and show a case study on how a 5G NPN and edge-enabled system can be used to realize an industrial vision detection system. In [25], different 5G-enabled solutions for factory manufacturing are discussed, including out-factory, in-factory, and integrated 5G network deployment options. In the latter case, the authors show how TSN and 5G networks can be integrated in order to ensure URLLC communication towards the factory local edge. As an illustrative example, the remote control of a gantry crane from the edge is also presented. In [26], the authors propose to use TSN as the networking solution to interconnect a fog computing node to its environment in a factory (e.g., sensors, actuators) in order to serve industry-grade applications. A detailed system configuration case study and its experimental evaluation are also shown. The authors in [27] propose IndustEgde, an edge-cloud collaborative intelligent (ECCI) platform system, where TSN is used for the link layer transport to reduce the system level latency for the real-time industrial applications. In addition to the detailed system design, two case studies for evaluating the effectiveness of IndustEdge have also been carried out.