# An Adaptive Control Scheme for Data-Driven Traffic Migration Engineering on 5G Network
## 3 SDN-Driven Traffic Migration Engineering (TME) Model
### Adaptive Traffic Migration Control Model  
Referring to Figure 2, the main principle of adaptive traffic migration control model is described below. After the data traffic prediction, the traffic prediction value \(\widetilde{\tau^{t}_{u,v}}\) in link \((u,v)\in E\) is obtained at time \(t\). Next, we compare peak rate \(R^{t}_{u,v}\) with \(\widetilde{\tau^{t}_{u,v}}\) in link \((u,v)\in E\). If the traffic in this link satisfies \(\widetilde{\tau^{t}_{u,v}}<R^{t}_{u,v}\), we do not migrate the data traffic in link \((u,v)\in E\); otherwise, the data traffic needs to be migrated. When predicted traffic \(\widetilde{\tau^{t}_{u,v}}\) in link \((u,v)\in E\) exceeds peak rate \(R^{t}_{u,v}\), it is necessary to find a new link in \(\eta(u)\) (the neighbors of OVS \(u\)), which meets the requirements of traffic and low latency, for replacing \((u,v)\) to complete the traffic transmission work. In this paper, we use the ITV principle to complete the traffic migration work, which is shown in Figure 3.  
Assume there are two OVSs \(v_{1},v_{2}\in\eta(u)\), satisfying \(\widetilde{t}_{u,v}^{\widetilde{\sim}}+\widetilde{t}_{u,v1}^{\widetilde{\sim}}<R _{u,v1}^{t}\), \(\widetilde{t}_{u,v}^{\widetilde{\sim}}+\widetilde{t}_{u,v2}^{\widetilde{\sim}}<R _{u,v2}^{t}\). We use \(D\) to represent the data traffic transmission latency of the link. If \(D(u,v_{2})<D(u,v_{1})+D(v_{1},v_{2})\), which does not violate the TIV principle, then we can migrate the traffic to link \((u,v_{2})\in E\) and delete link \((u,v)\in E\) directly, which is shown as Migration link (2) in Figure 3. Otherwise, if \(D(u,v_{2})>D(u,v_{1})+D(v_{1},v_{2})\), which violates the TIV principle, then the traffic cannot be migrated to the shortest link \((u,v_{2})\in E\), and it is necessary to migrate the traffic in link \((u,v)\in E\) to link \(u\to v_{1}\to v_{2}\), so as to complete the traffic migration locally. Algorithm 1 gives the pseudocode of the proposed TME algorithm in this paper. Here, we give the specific process description of Algorithm 1, which is shown as follows:  
**Step 1:** In TME, we input the coordinates and simulation parameters of each OVS in the network and the network will output the simulation results, such as the lifetime, throughput and latency. Initialize the network, use the greedy scheme to construct the data transmission links with the shortest path and run the network one time (lines 1-2). **Step 2:** For all OVSs, we predict traffic \(\widetilde{t}_{u,v}^{\widetilde{\sim}}\) of link \((u,v)\in E\) at time \(t\) (lines 3-13). **Step 3:** After the two-step modification of the traffic prediction, we migrate some traffics if the predicted traffics exceed the peak rate, so as to complete the traffic migration in the congestion link to a nearby link. Then, we finish the link reconfiguration in virtual network slicing (lines 14-18). Figure 3: Diagram of TIV migration Principle. ```
0: The coordinates and simulation parameters of each OVS and MBS. 0: The lifetime, throughput of the network and the residual energy resources of each OVS. 1:Initialization: Initialize the virtual network slicing. 2:Virtual network topology construction: Use the greedy strategy to construct the virtual network link model with the shortest delay and run the network for the first time. 3:for All OVSs do
4:if There is an OVS damaged or no energy resource. then
5: Terminate running the network and output data. 6:elseif Predict traffic \(\overset{\sim}{t}_{u,v}^{T}\) of link \((u,v)\in E\) at time \(t\).