Virtual network function chaining placement based on dynamic multi-objective optimization and multi-criteria decision making

Virtual network function chaining placement based on dynamic multi-objective optimization and multi-criteria decision making

연구 분야: Verification

학회: LANC '22: Proceedings of the 2022 Latin America Networking Conference

Network Function Visualization (NFV) is a technology that promises to provide greater flexibility and dynamism than the traditional, conventional networks with middlebox hardware. These benefits bring with them the challenge of calculating routes and locating multiple virtual network functions (VNF) in the network nodes to respond to complex traffic requirements. This problem is called virtual network function chaining placement (VNF Chaining Placement, VNF-CP) which has been discussed in the context of static optimization for both static and dynamic traffic. Given the dynamics and complexity of the VNF-CP problem, this paper proposes a framework that combines dynamic multi-objective optimization (DMO) and multi-criteria decision making (MCDM) in the process of solution deployment. The framework makes five actions in each operational cycle: it receives and analyses network traffic, determines the most relevant objective functions based on the traffic state, recomputes the non-dominated solutions set using a DMO algorithm, and finally selects a solution to deploy using an MCDM algorithm. In order to determine the effectiveness of the framework, the performance of dynamic multi-objective evolutionary algorithms (DMOEA) has been studied, state-of-the-art competitive (DNSGAII-A and DNSGAII-B) compared to traditional MOEAs, non-dynamic (NSGAIII, MOEAD, and REVEA) considering TOPSIS as MCDM scheme. The results of numeric simulations in the test instances show that the dynamic DMOEAs resolve the VNF-CP problem competitively and with promising results compared to traditional MOEAs.