Application of evolutionary mechanism to dynamic Virtual Network Function Placement

Recently, communication network services have become increasingly diverse and dynamic. Network Function Virtualization (NFV) is an effective technique to deal with these dynamic situations. Most related work on the VNF placement problem does not consider the dynamics of requests, but only static scenarios. The important goals of the dynamic VNF placement problem include accommodating new requests following the traffic dynamics and reducing the time to calculate solutions. To tackle this problem, we utilize the concept of Modularly Varying Goals (MVG), which is based on a genetic algorithm (GA) and generates solutions that can easily adapt to time-varying goals in short time. In this paper, we propose Evolvable VNF Placement (EvoVNFP) that applies the concept of MVG to the dynamic VNF placement problem to reduce the time to obtain solutions. Results from numerical evaluations show that our method is able to better follow the dynamics of VNF requests and also reduce time until adapting to successive objectives.