Automated Decision Making for the Multi-objective Optimization Task of Cloud Service Placement

The network functions virtualization (NFV) paradigm provides advantages with respect to aspects like flexibility, costs, and scalability of networks. However, management and orchestration of the resulting networks also introduce new challenges. The placement of services and virtualized network functions (VNFs) is a multi-objective optimization task that confronts operators with a multitude of possible solutions that are incomparable among each other. The goal of this work is to investigate mechanisms that enable automated decision making between such multi dimensional solutions. To this end, we investigate techniques from the domain of multi attribute decision making that aggregate the performance of placements to a single numeric score. A comparison between resulting rankings of placements shows that many techniques produce similar results. Hence, placements that achieve good rankings according to many approaches might be viable candidates in the context of automated decision making.