A Multi-criteria Decision Making Approach for Scaling and Placement of Virtual Network Functions

This paper investigates the joint scaling and placement problem of network services made up of virtual network functions (VNFs) that can be provided inside a cluster managing multiple points of presence (PoPs). Aiming at increasing the VNF service satisfaction rates and minimizing the deployment cost, we use both transport and cloud-aware VNF scaling as well as multi-attribute decision making (MADM) algorithms for VNF placement inside the cluster. The original joint scaling and placement problem is known to be NP-hard and hence the problem is solved by separating scaling and placement problems and solving them individually. The experiments are done using a dataset containing the information of a deployed digital-twin network service. These experiments show that considering transport and cloud parameters during scaling and placement algorithms perform more efficiently than the only cloud based or transport based scaling followed by placement algorithms. One of the MADM algorithms, Total Order Preference by Similarity to the Ideal Solution (TOPSIS), has shown to yield the lowest deployment cost and highest VNF request satisfaction rates compared to only transport or cloud scaling and other investigated MADM algorithms. Our simulation results indicate that considering both transport and cloud parameters in various availability scenarios of cloud and transport resources has significant potential to provide increased request satisfaction rates when VNF scaling and placement using the TOPSIS scheme is performed.