# A Novel Strategy for VNF Placement in Edge Computing Environments
## 2 Related Work  
The VNF placement problem is one of the most challenging issues in NFV and edge computing development. The literature presents proposals addressing such problems based on mono-objective or multi-objective approaches. As follows, we discuss such proposals. In Table 1, we depict a comparison among the VNF placement approaches. The last row of the table presents the features of our smart placement algorithm. Mono-objective approaches [14; 15; 16; 17; 30] solve the VNF placement problem by using only one characteristic of the environment, such as the resource consumption in edge nodes and, in this case, they try to minimize it. For instance, authors in [30] studied the VNF sharing problem aiming to minimize the cost for the mobile operator, subject to requirements on end-to-end service performance, e.g., total delay. [PERSON] et al. [16] proposed a solution to the VNF placement problem using an ILP model that guarantees the minimum end-to-end latency while ensuring QoS by not overstepping beyond an acceptable limit of latency violation. The authors used the ILP solutions to train an artificial neural network (ANN) to cope with the NP-hardness of such a problem. The authors in [18] also proposed an ILP model aiming to minimize the total resource consumption. Multi-objective approaches [19; 20; 21; 22; 23; 24; 25; 26; 27; 31] solve the VNF placement problem considering multiple, and sometimes conflicting, environment aspects. For instance, the authors in [31] demonstrated the gain of VNF sharing-based service function chaining (SFC) requests placement, as a way of satisfying more requests with average-less resources per request. They formulated the sharing-based SFC placement as an integer linear program (ILP) to minimize the overall deployment cost, but did not take the energy consumption into consideration. The authors in [20] formulated the joint problem of routing, and VNF placement cost minimization subject to flow demands where processing flows in local subnetworks is encouraged. [PERSON] et al. [21] proposed an SDN-NFV infrastructure to \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Objective** & **Approach** & **Model** & **Environment** & **VNF Sharing** \\ \hline [PERSON] et al. [14] & Mono & Genetic algorithm & No & Cloud & No \\ [PERSON] et al. [15] & Mono & Conventional & No & N/D & No \\  & & Light chain algorithm & & & \\ [PERSON] et al. [16] & Mono & Machine learning & Yes & Edge & No \\ [PERSON] et al. [17] & Mono & Greed & Yes & Edge & No \\ [PERSON] et al. [18] & Mono & Greed & Yes & Edge & No \\ [PERSON] et al. [19] & Multi & Markov chain & Yes & Edge and cloud & No \\ [PERSON] et al. [20] & Multi & Alternating direction & Yes & Edge & No \\  & & Method of multipliers & & & \\ [PERSON] et al. [21] & Multi & Genetic algorithm & Yes & Edge & No \\ [PERSON] et al. [22] & Multi & Machine learning & Yes & N/D & No \\ [PERSON] et al. [23] & Multi & Q-learning scheme & Yes & Edge & No \\ [PERSON] et al. [24] & Multi & Markov chain & Yes & Cloud & No \\ [PERSON] et al. [25] & Multi & Deep reinforcement & Yes & Cloud & No \\  & & Learning & & & \\ [PERSON] et al. [26] & Multi & Graph-based particle & Yes & Edge & No \\  & & Swarm optimization & & & \\ [PERSON] et al. [27] & Multi & Greed & Yes & Cloud & No \\ [PERSON] et al. [28] & Multi & Steiner Tree and Markov Decision & Yes & Cloud & Yes \\ [PERSON] et al. [29] & Multi & Greed & Yes & Edge & Yes \\ \hline
**Our approach** & **Multi** & **Smart Placement Algorithm** & **Yes** & **Edge** & **Yes** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the related work with our approach. tackle the VNF placement problem in wireless MEC networks by minimizing the overall placement and resourcing allocation costs. Unlike the already proposed approaches, our strategy tackles the VNF placement problem considering a set of aspects in the multi-objective approach that simultaneously minimize the energy consumption in the edge nodes, minimize the total latency, and reduce the total cost of the infrastructure.