# A Novel Strategy for VNF Placement in Edge Computing Environments  
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1 MidiaCom Laboratory, Institute of Computing, Universidade Federal Fluminense (UFF),  
Niteroi 24210-240, Brazil 1  
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in defined order). Hence, the concept of service function chaining (SFC) [2] emerges to denote a chain of services, i.e., a sequence of VNFs designed to replace a chain of physical equipment that provides a complete service requested by a user. Other technologies are commonly used to reap the benefits introduced by NFV. One of them is cloud computing, which allows the deployment of VNFs on data centers far from end-users. NFV and cloud computing can work with other technologies to provide a service-centric architecture. For instance, the edge computing paradigm extends the cloud computing paradigm by exploiting available resources, data, and computing at the network edge. Such an emergent paradigm preserves the key benefits of using clouds as support infrastructure while moving part of the processing and storage capabilities to the edge of the network, therefore repositioning services closer to end-users and data sources. The significant benefits of edge computing include reduced communication delay, the use of less network bandwidth, control decisions at the network edge, higher security, and data privacy, as presented in [3]. Moreover, edge computing can utilize edge node collaboration to compensate for their lower resource capacity. When integrating NFV and edge computing, VNF instances are deployed in hosts called edge nodes. Such a strategy facilitates the QoS fulfillment of time-critical applications, given the proximity to end-users, while taking advantage of the often idle capabilities of the various computing nodes deployed at the network edge. According to [4], the notion of edge/fog nodes is wide, with any equipment with processing power and storage capacity acting as an edge node. Different management and orchestration challenges arise in such virtualized and distributed environment. One of these challenges concerns the placement of VNFs in edge nodes, which consists of selecting the most suitable edge nodes to deploy VNFs to meet service requests from multiple applications and users [5]. Typically, the component responsible for the placement is the network function virtualization orchestrator (NFVO) [6]. This must be performed in such a way that the QoS requirements of the applications are met without service-level agreement (SLA) violations, at the same time that the use of service providers' resources at the edge tier is optimized. Note that no single optimal solution exists given that this is a multi-objective optimization problem, i.e., an optimization problem with multiple and conflicting objectives, as defined in [7,8]. Several authors proposed different optimization models and heuristic solutions for managing the VNF placement problem [9, 10, 11, 12, 13]. In this paper, we address such a problem by providing a novel integer linear programming (ILP) optimization model and a novel heuristic method to provide more scalable solutions in a viable time. Our approach considers users with heterogeneous demands regarding network latency and bandwidth accessing a system. The edge nodes also have heterogeneous CPU, memory, latency, and bandwidth capacities. Additionally, each edge node has a different subset of VNF types (e.g., virtual machine images or container images) that can be offered on demand. Furthermore, each VNF request has a set of requirements that must be addressed. Some requirements are intrinsically related to the VNF, for example, the minimum amount of CPU available in the edge node. In contrast, others are application-dependent, for example, the maximum tolerable delay. Our formulation also defines that the placement occurs after the arrival of multiple users' requests. All resources of the edge node are available for running new VNF instances before the placement decision.