## 2 Related Work  
Recently, with the advancement of virtualization technology, many researches have focused on optimal VNF placement and traffic routing, which are typical resource management problems. We divide such technology into traditional approaches and intelligent algorithms utilizing artificial intelligence. The first class of techniques can be further divided into those that use mathematical optimization and those that use heuristics. Most research models the optimization problem mathematically according to different optimization objectives and resource constraints. Deterministic methods such as binary integer programming (BIP) [10; 20; 21] or mixed ILP (MILP) [22] are generally used. However, since the dynamic SFC placement is an NP-hard problem, the optimal solutions of these methods are not obtained efficiently on a large network scale. Some research has considered different approximate strategies to reduce computation time [9; 12; 22; 23]. For example, in order to meet the demand for low-latency services, the joint service placement and request routing problem in multicell MEC networks, aiming to minimize the load of the centralized cloud, was formulated by [PERSON] et al. [23]. However, they did not consider load balancing between the cellular BSs. Similarly, the problem of placing VNFs on edge and public clouds and routing the traffic among adjacent VNF pairs was investigated by [PERSON] et al. [12]. They defined this problem as the delay-aware VNF placement and routing (DVPR) problem. They also proved that the DVPR problem is NP-hard and presented a randomized rounding approximation algorithm to solve it in polynomial time. However, they did not consider the resource consumption cost when instantiating VNFs. Considering the SFC ordering constraints of all the network flows, [PERSON] et al. [9] addressed the problem of SFC placement using an efficient algorithm based on linear programming (LP) rounding, which achieved a logarithmic approximation factor. As mathematical programming approaches have a high time complexity, heuristics have been employed to address the challenges associated with VNF placement and traffic routing problems in the majority of research papers [2; 10; 13; 24]. Such methods are generally effective and achieve good performance in terms of execution time. For example, [PERSON] et al. [2] accounted for multiple resources and QoS constraints in NFV-enabled SDNs to achieve SFC request flow (SR) routing. A VNF-split multistage edge-weight graph and a cost model considering the resources of nodes and links were innovatively constructed by the researchers. In [24], the impact of user association on SFC placement was investigated by [PERSON] et al., who proposed scalable heuristics to efficiently find a near-optimal solution within polynomial time. A heuristic algorithm was devised by the authors of [10] to formulate the problem of network throughput maximization, considering both horizontal and vertical scaling. Nevertheless, the delay of each link was negligible in their work. Instead of studying VNF placement and traffic routing at the same time, some works resort to a two-stage method [10; 13; 25], wherein VNFs are placed first, then links are established. In [13], in order to make a tradeoff between the resource consumption of servers and links, the authors proposed a two-stage VNF placement scheme where a constrained depth-first search algorithm (CDFSA) is employed to find all feasible paths between the source and the destination and a path-based greedy algorithm to sequentially assign VNFs with minimum resource consumption. In [25], the authors proposed a new concept, hybrid SFC, and used a heuristic algorithm to solve the dynamical SFC embedding. Unfortunately, as they adopted the shortest path or a greedy algorithm, it is difficult to guarantee the load balancing of the network. In recent years, some researchers have applied machine learning to solve multiobjective optimization problems [18; 19], such as computing offloading [26], traffic routing, and SPC orchestration.