# Intelligent Computation Offloading Based on Digital Twin-Enabled 6G Industrial IoT
## 5 Experiments and Discussion
Local processing scheme: all computing tasks are performed by local devices, and DT is not enabled. The simulation scenario is a normal 6G IIoT network. 2. Nearest edge server selection scheme: in a DT-enabled network architecture, the nearest edge server is selected to offload the computation tasks. Figure 6 depicts the end-to-end delays under different offloading strategies. The proposed DT-enabled DDPG algorithm is compared with two benchmark algorithms. From the figure, we can see that in all three scenarios, the end-to-end delays lengthen as the amount of task computation requirement increases. When the network throughput reaches equilibrium, the end-to-end delays stay at their maximum values, which are 175.51 s, 108.23 s, and 79.82 s, respectively. This is because as the amount of task computation requirement increases, the system needs more time to process the data. When the task computation requirement is a constant, the end-to-end delay of the proposed DT-enabled DDPG algorithm is the smallest, followed by the DT-enabled nearest selection, and the end-to-end delay of the local processing is the longest. This is because the local processing capability is limited, and although the transmission delay associated with offloading tasks is avoided, the processing delay is the longest. The factors to consider in DT-enabled  
nearest selection are relatively simple, which may cause an edge server to become too busy and increase its processing delay. Figure 7 illustrates the variation of end-to-end delay with the change in the number of edges servers in different scenarios. Since this scenario requires the participation of the edge server, we compare the variation of end-to-end delays when the transmission powers are different. As can be seen from the figure, when the transmission power is larger, the end-to-end delay will be reduced, because the transmission delay is reduced. The gap between the two schemes increases as the number of edge servers increases, with a maximum gap of about 33.52 s. Similarly, when the transmission power is constant, the delay of DT-enabled DDPG is smaller than that of the DT-enabled nearest selection. For example, when the transmission power is 0.1, the maximum difference between the two schemes can reach 36.25 s. As the number of edge servers increases, it brings more system processing capability, so the end-to-end delay decreases. Figure 8 depicts the variation of end-to-end delay with the change of the edge server CPU cycle frequency. Since the changes in the processing capability of the edge servers also have no impact on the local processing, we set the scenarios to be the same as in Figure 7. As can be seen from the figure, as the CPU cycle frequency increases, the end-to-end delay decreases. The value is reduced from a maximum of 98.01 s (0.1 W) to a minimum of 49.98 s (0.2 W). When the transmission power remains the same, the difference between the two schemes is about 20 s to 30 s. This is due to the increased processing capability of edge servers, which reduces the processing delay in the total end-to-end delay. Figure 6: End-to-end delays under different task computation requirements. Figure 7: End-to-end delays under different numbers of edge servers. Figure 9 shows the influence of different offloading strategies on the task success rate. It can be seen from the figure that the task success rate decreases as the amount of data generated for the task increases. The increased amount of data means that since more network resources are needed to process it, some tasks are discarded because they exceed the maximum delay bound. This can be improved by increasing the processing capability of the CPU (both local and edge). In the case of the same amount of task computation requirement data, local processing has the longest end-to-end delay, so it has the lowest success rate, which is from 86.43% to 57.03%. Algorithm DT-enabled nearest selection has an intermediate success rate, which is from 92.47% to 63.87%.