## 5 Evaluation
### Evaluation Result of DQN-EC
Conversely, DQN-EC can maintain the congestion probability as low as possible throughout learning, and thus the network throughput is not reduced by up to 15%, which is the maximum probability of a switch failure, and can continue to improve, reaching up to 515 MB/s. The performance of the recovery times that occur during decoding using optimized routing paths is assessed. The recovery time is the sum of the network bottleneck costs and the link count costs and is compared and evaluated using OSPF routing network algorithms. The relevant content is shown in Equation (11). Because the measured recovery time varies depending on the probability of failure of the switch, the probability of a switch failure was fixed and measured at 5% to maintain a constant block transfer time. \[ \begin{array}{l}RT=\sum_{r=1}^{n}(BB_{r}+TL_{r}),\\ BB_{r}=argmin(\sum_{k=1}^{n}NB_{k}),\\ TL_{r}=\frac{\sum_{k=1}^{n}NB_{k}}{\sum_{k=1}^{n}L_{k}},\end{array}  \tag{11}\]  
where \(BB_{r}\) indicates the bottleneck bandwidth, \(TL_{r}\) indicates the transmission link, \(NB_{k}\) indicates the network bandwidth and \(L_{k}\) is the number of links. Above, \(RT\) is the recovery time, which is the sum of the \(BB\) and \(TL\) costs, where the former indicates the bottleneck bandwidth in routing paths, and the latter indicates the transmission link costs in the routes. The lower the measured value, the better, because the network bottleneck bandwidth indicates the bandwidth at the point where network traffic occurs at a particular point while configuring the network routing path. The transmission link cost is the cost of summing the measured network bandwidth in the optimized routing path and dividing the total sum of the link counts of the links. The results related to network bottlenecks and transmission link costs are shown in Figure 11.  
Figure 10: Network throughput based on the probability of a link failure. Figure 11a shows that the network bottleneck bandwidth of DQN-EC averaged 493 Mbps, which is slightly lower than that of OSPF at 633 Mbps. The network bottlenecks were reduced compared to OSPF because under intense traffic decoding operations using optimized routing paths that avoid high transport links are applied as much as possible. Figure 11b shows that the transmission link costs measured are high in OSPF, which can simply maintain short transmission links up to two recovery requests. However, the more recovery requests that exist, the more network routes that occur, and the greater the complexity, the lower the DQN-EC, which considers the bandwidth and transport links. In addition, the bottleneck bandwidth is likely to be due to the processing limitations of the internal bus bandwidth of the deep learning workstation. Therefore, checking the GPU, memory, and GPU entries in the monitoring log recorded during this experiment, it was shown that a pull-load phenomenon did not occur, and thus was less relevant to the internal bus bandwidth. Figure 12 shows the combined recovery time for network bottlenecks and link count costs. The measurement of the recovery time of DQN-EC and OSPF gradually varies as the number of recovery requests increases. DQN-EC takes 77 s to recover when the maximum number of recovery requests is 10, whereas OSPF requires 165 s for the same number of maximum requests. The optimized routing path of DQN-EC avoids bottlenecks as much as possible and chooses links in which the transmission link costs are low. Therefore, even if the bandwidth of the transfer link is low, the recovery time is shorter than that of the OSPF used to reduce the cost of the simple transfer link. Figure 11: Graphs of \(BB_{r}\), \(TL_{r}\) by equation 11 as: (**a**) Bottleneck bandwidth based on number of decoding requests; (**b**) Transmission link cost based on number of decoding requests. Figure 12: Recovery time based on number of decoding requests.