# Policy-Based Composition and Embedding of Extended Virtual Networks and SFCs for IIoT
## 3 Problem Description
### Use Case  
Industry 4.0 enterprise applications will support the smart factory through data analysis and autonomic decision making. For this purpose, monitoring, analysis, and management functions at different levels of the enterprise are needed. Remote asset management is required when the administrators manage different distant locations to remotely control customizable production, safety, energy consumption, utilization, and security. IIoT enables remote asset monitoring by installing wireless sensors throughout the factory and integrating them via an Internet gateway. The data can be remotely accessed to take further actions in real-time. However, in addition to the application functionsin such a scenario, the service provider shall be able to create and deploy the SFC that can satisfy the specific requirements of the industrial enterprise, such as security and resilience of the monitoring traffic and resultant decisions. Virtualization technologies support the reliability and flexibility of the management applications by deploying data acquisition and analysis functions at the edge and central data centers, respectively. In this scenario, the central data center hosts enterprise functions, while the edge data center hosts individual factory functions. This solution enables the seamless transport of data to an analysis environment. Asset management is the process of tracking the physical assets and making smart decisions based on the gathered data from the assets and environment. Asset management has been listed as one of the five top industrial IIoT use cases by IBM [14]. In asset management, information about assets are actively tracked without any human involvement. Several types of sensors, such as temperature, humidity, pressure, and proximity sensors, are used to gather data that is transferred to edge/cloud computing for making smart decisions. Three levels of data processing are represented in this use case: factory hall, edge computing, and cloud computing. Furthermore, the factory hall includes an IIoT gateway and several types of devices (such as sensors and cameras) that produce or consume certain rates of data. The use case also represents multiple locations, and each location might include either multiple factory halls and one edge computing center, or a cloud computing center. The edge and cloud computing levels are assumed to host data analysis and decision making functions. Our use case includes three locations with multiple domains that belong to the three mentioned levels, as depicted in Figure 1.  
_The cloud computing level_ that is located in a separate location (location 1 here) performs heavy analysis on the gathered data from the manufacturing locations (2 and 3 here). The analysis results are used for making strategical enterprise decisions, for example, related to customized production. _The edge computing level_ that is located in each manufacturing location (2 and 3) performs light analysis on the gathered data from the factory halls of this location. The analysis results are used to track the locations of assets and employees and environmental data to predict/detect anomalies in real-time, which are mainly related to safety and security. Edge computing can provide local data analysis with accepted latency in order to activate certain actions in real-time using the actuators in the factory hall. These actions are required for cases that cannot be detected by the devices in the factory hall and require data gathering and analysis. For example, detecting physical security breaches by tracking the placement of certain assets. Additionally, because industrial big data are often unstructured, they can be compressed and filtered by the edge computing before sending it to the cloud [15]. In the _factory hall level_, the IIoT platform allows for easy control and management.