# VNF Placement Optimization at the Edge and Cloud +
## 1 Introduction
One major concern when solving the VNF Placement problem in such an environment is how to efficiently utilize the resources between the MEC and Cloud infrastructures in order to reconcile the conflicting goals of latency performance and deployment cost. This is a major challenge even if the Cloud was the only infrastructure available. For example, a  
Figure 1: Cloud-based Internet of Things platform. Service Provider (SP) needs to balance the conflicting goals of minimizing the cloud resources used (e.g., for reducing the overall deployment cost) and minimizing the end-to-end delay for meeting pre-specified service-level agreements (SLA) [12; 13]. However, minimizing the number of used servers can lead to an over-utilization of the server resources that can have a serious effect on the overall network delay [14]. The existence of both the MEC and Cloud creates an extra level of complexity when trying to balance the two particular metrics. On the one hand, utilizing the resources of the MEC can significantly reduce the perceived propagation delay. However, the MEC has a limited amount of resources and over-provisioning can lead to overutilization of the underlying infrastructure. On the other hand, the Cloud has many more available resources, but are usually remote from the IoT devices adding delay overhead. Furthermore, there are additional constraints that should be taken into consideration during the deployment of the SCs. For example, a subset of VNFs may come with location constraints. In this case, some VNFs must be deployed at the reliable and more resource powerful Cloud Computing layer (e.g., video optimization, deep packet inspection [15]), while others need to be instantiated close to the end users at the MEC (e.g., network address translation-NAT, firewall, traffic prioritzaion) [16; 4]. The topological characteristics of the SC can impose further limitations. Until now, most of the existing works assume that an SC is a simple line sequentially connecting several VNFs. However, more realistic scenarios include topologies with bifurcated paths (i.e., service graphs) [17]. Furthermore, the rate of the data can change according to the type of VNF processing that is applied. For example, a firewall can drop untrusted data streams resulting in flows with lower data rate, and video rendering VNFs can change the encoding of the traffic leading to higher/lower data rates [18]. These are factors that should also be considered, especially in a delay sensitive environment such as IoT. To this end, we propose a novel resource allocation strategy to reconcile the conflicting goals of concurrently minimizing overall deployment costs and delays. In contrast with other existing approaches, we take into consideration a realistic scenario, incorporating a complete set of constraints, while we extend our framework and incorporate new directions in an intermix of different platforms and requirements. The main contributions of the paper are listed as follows:  
* Extend the VNF Placement problem taking into consideration the existence of both the Edge and Cloud infrastructures (VNF Placement at the Edge and Cloud VNF-PEC). * Introduce an optimal placement algorithm formulated as a Mixed Integer Programming problem and a sub-optimal but fast approach based on the Tabu Search meta-heuristic. * Take into consideration realistic SC topologies with features that can modify the incoming and outgoing data rate. * Provide additional constraints regarding the location requirements imposed by the VNFs. * Formulate the total experienced delay by considering all different delay levels during this end-to-end communication. * Create a realistic experimentation environment using real data for the resource characteristics of the MEC and Cloud infrastructures and for the requirements of the IoT traffic. The remainder of the paper is organized as follows: in Section 2, we present a detailed overview of the related work. Section 3 provides the system model followed to perform the VNF-PEC optimization.