# An Adaptive Control Scheme for Data-Driven Traffic Migration Engineering on 5G Network
## 2 Related Work
[19] propose a re-configuration strategy for the 5G network slices, which takes the network latency and bandwidth allocation as constraints in its constructed traffic problem model, establishes a feasible and orderly configuration sequence from the original slicing, and then migrates virtual OVSs and links dynamically. For multiple distributed controllers, [PERSON] et al. [20] propose a prediction-based SDN load balancing dual-weight switch migration scheme. The scheme can quickly balance the load between controllers and reduce the number of switch migrations. [PERSON] et al. [6] propose a service-oriented TE scheme based on SDN-NFV integration for 5G network. In a data center (DC) virtual network, the classical A* Search algorithm [21] can resolve the problem of traffic link allocation efficiently. This algorithm is a heuristic algorithm that places all nodes in the Open set and Close set. The Open set stores the nodes that have been selected, and the Close set stores the nodes that have not been selected. Then, the authors explore the links from the source node to the destination node to obtain the optimal data traffic link according to the priority level of nodes. Latency is the main KPI in TE-based DC network, [PERSON] et al. [22] summarize the future DC networking from low latency to deterministic latency. This article focuses on effective latency reduction design, which mainly aims at reducing the queuing delay in switches. Aiming at Egress Peer Engineering (EPE), [PERSON] et al. [23] allocate the network traffic with different destinations to the constructed internal autonomous routing system. This system considers the dynamic property and unpredictability of network traffic, and updates the traffic allocation to meet the minimum OPEX. In addition, the TE algorithm has great applications, such as smart city [24], intelligent community, Industrial Internet of Things (IIoT) [25; 26; 27; 28; 29], some vertical business services [30; 31; 32], and so on. [PERSON] et al. [33] introduce a Quality of Service (QoS)-aware network configuration and multipath forwarding method, and then propose a resource allocation technology, HPR, based on SDN. The HPR algorithm provides the efficient network configuration by exploiting the Dijkstra shortest-path algorithm. On the premise of the reasonable computing time, the HPR algorithm ensures the efficiency with lower OPEX. However, HPR algorithm does not consider the overall network traffic load balancing and resource utilization. To this end, it is necessary to further explore the optimization scheme for such problems. The main purpose of a TE-based network is to achieve the mission objectives given by users and rapidly allocate the network resources through TE optimally. Most of the existing solutions have shortcomings in the aspects of feasibility and scalability, such as the joint optimization of TE, resources and KPIs. Especially in the traffic control phase, the network resources such as energy of nodes are greatly affected by traffic. Moreover, in the traffic control phase, load balancing is greatly affected by traffic, therefore, it is necessary to combine TE with resource allocation to obtain the optimal KPIs. In the actual NFV networks, prediction and migration should be harmonized. It would be more efficient in resource allocation of the 5G NFV networks if the traffic were predicted before being migrated. Based on the above observations, in this paper, we first adaptively predict the link traffic at time \(t\), and then carry out the traffic migration based on the TIV theorem and traffic prediction at time \(t-1\). This is expected to improve the usage rate of network resources. The measurement accuracy of TME is improved by modifying the traffic prediction. The constructed systems will serve the intelligent transportation system based on TE technology.