# An Adaptive Control Scheme for Data-Driven Traffic Migration Engineering on 5G Network
## 1 Introduction  
The 5G network is a symmetric cellular communication network, which is defined by the 3 rd Generation Partnership Project (3 GPP) [1]. The multiple-input multiple-output (MIMO) technology of the 5G network provides hardware and software support for the diverse supplies and demands in the development of modern society. The 5G intelligent network is a kind of radio access network (RAN), which has good performance in mass data transmission [2]. The management system of the 5G network can recognize different network interface information. Moreover, the constructed network can meet the demands of KPIs for diversified resource allocation technologies. Due to the excellent performance of the 5G network, the applications of key technologies based on 5G network are more and more extensive, such as spatial information networking technology, smart city, internet of vehicles, Internet of Things (IoT), artificial intelligent (AI), soft computing, fog radio access network optimization, user plane functions in 5G networks, beyond wireless networks and the depth of open-source networks [3, 4, 5, 6]. And these technologies can serve aerospace, navigation, transportation, navigation, positioning, emergency rescue, intelligent life and other aspects. Moreover, the technologies mentioned above have high requirements for the network of high-level dynamicity, low latency and reliable transmission and high-density device access. Involved with standards, specifications, modes, tools and data sets, another key requirement for the 5G network is the rapid allocation of network resources in order to achieve optimal performance [7]. That is, the 5G network requires the optimal configuration and allocation of network resources such as communication, computing and storage [8]. This paper intends to virtualize a 5G network in consideration of eMBB network application scenarios, and then builds an adaptive TME system for reconfiguring the 5G virtual network. In order to meet the optimal network resource allocation and the adaptive control of network energy consumption at the same time, we provide a solution of 5G virtual network energy management to adaptively control the energy consumption. Firstly, we map the mobile communication software definition network (SDN) devices to open virtual switches (OVSs) and predict the link traffic on the virtual network at time \(t\) by using the [PERSON]'s second theorem, which related to previous time \(t-1\). Then, we modify the predicted traffic model by two steps. Finally, based on the TIV theorem [9], we perform traffic migration when the predicted traffic exceeds the peak rate in a virtual link. Through the above traffic prediction and migration engineering, it is expected to reduce traffic congestion and improve the efficiency of the network energy management, delay and throughput. The main contributions of this paper are the following:  
1. In this paper, the TE problem is considered as the principal aspect of resource allocation in the 5G virtual network. We simply establish the associated traffic topological model through comprehensive consideration of traffic prediction and migration to complete the optimization task of data transmission;
2. A simple and efficient TE technology is proposed for the network slicing. To improve the efficiency of data traffic migration, a modified adaptive traffic prediction model is used to predict the traffic in the virtual link of the network. Then, based on the triangle inequality violation theorem, some local network traffic is adaptively migrated when the predicted link traffic exceeds the peak rate. Finally, the migrations of logical links in the virtual network layer are completed;
3. To the best of our knowledge, this is an early effort in considering how to change the configuration of the virtual slices in a timely fashion, while including the TE, such as latency, throughput and resource allocation of the network. The remainder of this paper is arranged as follows.