# Policy-Based Composition and Embedding of Extended Virtual Networks and SFCs for IIoT
## 1 Introduction  
The traditional legacy networking paradigm of deploying proprietary and hardware-based network functions is not flexible enough to satisfy the emerging and ever-growing application requirements, mainly the security, performance, and resilience requirements of 5G mobile networks and Industry 4.0. This is due to the complexity and high cost of deploying new devices, upgrading devices, and service innovation. Network Function Virtualization (NFV) is a modern technology that decouples the network functions from the hardware. It builds Service Function Chains (SFCs) of Virtual Network Functions (VNFs) in order to provide end-to-end network services to applications. The SFC might include one or more sub-SFCs that define the data flow paths that are based on traffic specifications. NFV leverages virtualization technologies to flexibly deploy complex network functions on-demand in the required locations. The network functions are deployed in the form of virtual machines or containers on standard servers. The SFC composition problem has already been discussed by researchers without addressing different application- and service provider-driven policies. Such policies might be required, for example, to place certain VNFs in certain locations or to add certain security functions accordingto the traffic path and application security requirements. From another perspective, solving the SFC composition problem without considering the application functions imposes inconsistency between the composed SFCs and the application requirements. For example, when the application functions are also virtualized, some functions might need to be instantiated over multiple servers for fault tolerance, load balancing, or server capacity constraints. Such a case requires modifications to the composed SFC, for example, by adding a load balancing VNF between the SFC and instances of the application function. The problem of embedding a SFC is similar to traditional Virtual Network Embedding (VNE) problems. In VNE, the physical network (Substrate Network (SN)) and the Virtual Network (VN) to be embedded (overlay with Virtual Nodes (VNos) and Virtual Links (VLis)) are represented as graphs. VNE algorithms apply optimization and heuristic methods to find the optimal mapping of the VN on the SN with different objective functions, such as admission ratio, embedding costs, path length, or delay. VNE algorithms are needed in NFV to perform the autonomic composition and embedding of SFCs based on the application requirements and service provider policies. In this paper, we present a preliminary model that combines NFV with the node and link mapping approaches from VNE. This model transforms an abstract Application Request (AR) that defines certain properties of end-nodes, such as type and location/domain, and it might define preliminary security, low latency, and redundancy requirements. The AR is converted while using graph transformation method to an Extended Virtual Network (EVN) that combines the application end-nodes (physical or virtual) with the composed SFCs based on the rules defined by the service provider. The graph transformation sequentially builds the EVN from the AR based on policies that are related to devices'/functions' types and locations/domains, redundancy, low latency, security-related network functions, and load balancing. The communication services among VNos are realized via VNF requests. Several Forwarding Graphs (FGs) are possible based on the created VNF request and dependencies among VNFs. We present an approach for creating all possible FGs while using topological sorting. The EVN embedding algorithm first embeds the application VNos and VLis that do not include VNF requests. Subsequently, the SFCs (FGs) are embedded using a greedy proactive heuristic that is independent of the chain and path lengths. The model is supported by a use case, remote asset management, inspired by the principle of smart factory in Industry 4.0 and based on Industrial Internet of Things (IIoT) concepts.