Network Function Virtualization (NFV) is an emerging technology to consolidate network functions onto high volume storages, servers and switches located anywhere in the network. Virtual Network Functions (VNFs) are chained together to provide a specific network service, called Service Function Chains (SFCs). Regarding to Quality of Service (QoS) requirements and network features and states, SFCs are served through performing two tasks: VNF placement and link embedding on the substrate networks. Reducing deployment cost is a desired objective for all service providers in cloud/edge environments to increase their profit form demanded services. However, increasing resource utilization in order to decrease deployment cost may lead to increase the service latency and consequently increase SLA violation and decrease user satisfaction. To this end, we formulate a multi-objective optimization model to joint VNF placement and link embedding in order to reduce deployment cost and service latency with respect to a variety of constraints. We, then solve the optimization problem using two heuristic-based algorithms that perform close to optimum for large scale cloud/edge environments. Since the optimization model involves conflicting objectives, we also investigate pareto optimal solution so that it optimizes multiple objectives as much as possible. The efficiency of proposed algorithms is evaluated using both simulation and emulation. The evaluation results show that the proposed optimization approach succeed in minimizing both cost and latency while the results are as accurate as optimal solution obtained by Gurobi (5%).

网络功能虚拟化（NFV）是一种新兴技术，可将网络功能整合到网络中任意位置的大容量存储，服务器和交换机上。虚拟网络功能（VNF）链接在一起以提供一种称为服务功能链（SFC）的特定网络服务。关于服务质量（QoS）要求以及网络功能和状态，SFC通过执行两项任务来提供服务：VNF放置和链路嵌入在基板网络上。对于云/边缘环境中的所有服务提供商而言，降低部署成本是增加其从所需服务中获利的理想目标。但是，为了减少部署成本而增加资源利用率可能会导致服务延迟增加，从而增加违反SLA的行为并降低用户满意度。为此，我们针对联合VNF放置和链接嵌入制定了一个多目标优化模型，以针对各种约束降低部署成本和服务延迟。然后，我们使用两种基于启发式的算法来解决优化问题，这些算法在大型云/边缘环境中的性能接近最佳。由于优化模型涉及相互矛盾的目标，因此我们还研究了pareto最优解决方案，以便它尽可能地优化了多个目标。使用仿真和仿真来评估所提出算法的效率。评估结果表明，所提出的优化方法成功地最小化了成本和等待时间，而结果与Gurobi（5％）获得的最优解决方案一样准确。