Network function virtualization enables network operators to implement new services through a process called service function chain mapping. The concept of Service Function Chain (SFC) is introduced to provide complex services, which is an ordered set of Network Functions (NF). The network functions of an SFC can be decomposed in several ways into some Virtual Network Functions (VNF). Additionally, the decomposed NFs can be placed (mapped) as VNFs on different machines on the underlying physical infrastructure. Selecting good decompositions and good placements among the possible options greatly affects both costs and service quality metrics. Previous research has addressed NF decomposition and VNF placement as separate problems. However, in this paper, we address both NF decomposition and VNF placement simultaneously as a single problem. Since finding an optimal solution is NP-hard, we have employed heuristic algorithms to solve the problem. Specifically, we have introduced a multiobjective decomposition and mapping VNFs (MODMVNF) method based on the non-dominated sorting genetic multi-objective algorithm (NSGAII) to solve the problem. The goal is to find near-optimal decomposition and mapping on the physical network at the same time to minimize the mapping cost and communication latency of SFC. The comparison of the results of the proposed method with the results obtained by solving ILP formulation of the problem as well as the results obtained from the multi-objective particle swarm algorithm shows the efficiency and effectiveness of the proposed method in terms of cost and communication latency.

本研究解决了服务功能链映射过程中网络功能分解和虚拟网络功能布置之间缺乏协同的问题。提出了一种基于非支配排序遗传多目标算法的多目标分解与映射虚拟网络功能（MODMVNF）方法，以同时最小化映射成本和通信延迟，显著提高了效率与有效性。研究结果表明，该方法在成本和通信延迟方面优于传统方法。

通过同时进行网络功能分解和虚拟网络功能布置优化服务功能链映射