# An Adaptive Control Scheme for Data-Driven Traffic Migration Engineering on 5G Network
## 3 SDN-Driven Traffic Migration Engineering (TME) Model
### The Description of TME  
The optimization of network resource allocations based on TE is a complicated multi-objective, multiconstraint and non-convex optimization problem [15]. The prediction and migration processes are limited by the traffic controller and the constraints of different KPIs, such as peak rate and latency. In addition, a dynamic traffic prediction and migration framework is designed for network slices, which needs to be flexible enough and allows the network resource allocation among slices, customers, or services according to different purposes, or specific operational requirements in different application scenarios. As the 5G network services need to be improved in terms of time latency and node energy efficiency, one of the above optimization problems must be solved in order to supply resources to slices successfully [16]. In order to allocate the network resources through the joint optimization of resources and TE, we must complete the resource scheduling through the high-capacity network link interconnection by distributed computing. Finally, the resources are allocated optimally through the optimal configuration and the optimal migration of TE [21]. Based on the basic theory research of network [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33], we aim to break through the bottleneck of network resource allocation technology. Therefore, we turn to developing a service protocol framework, which is conducive to the development of the above technologies and provides theoretical and technical reference for the development of the above technologies through the exploration of new methods and theories.  
The general meaning of traffic migration in virtual network slice layers is shown in Figure 2.  
The detailed descriptions for traffic migration are as follows. When the traffic in logical link \((u,v)\) exceeds its peak rate, the virtual link will suffer from data congestion and lead to the increase of the data transmission latency and even the losses of data. Therefore, we need to migrate the traffic in virtual logical link \((u,v)\). However, the migration is limited to the power of OVS and we can only find and establish a new data transmission link in the neighbor of OVS \(u\). If we find an appropriate OVS \(v_{1}\) in \(\eta(u)\) whose traffic does not exceed the peak rate of the newly built virtual link and this link also has a low time latency, then the traffic in \((u,v)\) will be migrated to the newly built link \((u,v_{1})\). Finally, we complete the traffic migration and delete the original link \((u,v)\), and then use the newly built link \((u,v_{1})\) for data traffic transmission.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notations** & **Descriptions** \\ \hline \(G(V,E,B,R)\) & The graph which shows the virtual weighted network model \\ \(V\) & The set of OVSs in the network \\ \(E\) & The set of links (edges) in the network \\ \(B\), \(R\) & Associated weights of each link \\ \(|E|\) & The number of links in the network \\ \(B_{u,p}\) & The bandwidth of link \((u,v)\in E\) \\ \(R_{(u,v)}\) & The peak rate of link \((u,v)\in E\) \\ \(R_{u,p}^{t}\) & The peak rate of link \((u,v)\in E\) at time \(t\) \\ \(X_{u,p}^{t}\) & A boolean variable to decide whether OVS \(u\) selects \(v\) as parent or not \\  & \(X_{u,p}^{t}=\{\begin{subarray}{c}1\\ 0\end{subarray}\text{ }