## 5 Evaluation
### Evaluation Result of DQN-EC  
This section introduces the results of the rate of change in compensation per episode associated with the designed DQN neural network model. It also introduces the time and network throughput results of transferring blocks from the erasure coding network topology to the target nodes, depending on the probability of a switch failure. Finally, we introduce the recovery time results based on the number of recovery requests. Figure 8 shows the cumulative change in reward values according to the learning process of the agent when using DQN-EC and OSPF. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Configure Value** \\ \hline Training steps & 100 \\ DQN episodes & 100 \\ Link maximum bandwidth & 1 Gbps \\ Link real-time bandwidth & 300–600 Mbps \\ Traffic intensity & 30–50\% \\ Probability of link failure & 0–15\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters of network topology simulation. Figure 7: Example of applying EC to fat-tree network topology. Figure 8 shows the results of measuring the change in the cumulative reward value, with the \(x\)-axis representing the number of episodes and the \(y\)-axis representing the sum of the rewards according to the number of episodes. On average, 95 different routing paths were generated, some quite similar, and some completely different. However, as the number of episodes was repeated, a similar route was created in the last 80-100 paths. Using the e-greedy method, the agent initially does not have sufficient knowledge of the current network topology. Thus, most of agents explored the environment and obtained a lower reward value; however, as the number of episodes increased, the reward value increased and converged to the maximum value, indicating that optimized network routing paths were created during the last episode. Figure 9 shows the block transfer time according to the probability of failure of 0% to 15% on a switch corresponding to the simulation parameters when using DQN-EC and OSPF. Figure 9 shows the measurement of the block transfer time from the source node to the destination node according to the probability of a switch failure, in which the decoding request was conducted only once. When a decoding request is made, nine blocks with a size of 128 MB are sent to the destination node. Compared to the existing OSPF routing algorithms, the block transfer time is reduced. For up to 15% of the maximum probability of a switch failure, DQN-EC has a slight increase in the block transfer time from 5.2 to 11.8 s. However, OSPF increases significantly from 12.48 to 30.68 s as it reaches 15%. DQN-EC measured a block transfer time of 8.25 s on average, and OSPF averaged 20.91 s, showing a  
Figure 8: Total reward based on number of episodes. Figure 9: Block transmission time based on the probability of a link failure. difference in the block transfer rate of approximately 2.5. The DQN-EC neural network model provides a faster block transfer time than the Dijkstra algorithm-based shortest-path OSPF because the agent avoids failed switches and selects links corresponding to optimized routing paths when considering the bandwidth. Figure 10 shows the network throughput according to the probability of a failure of 0% to 15% on a switch when using DQN-EC and OSPF. Figure 10 shows the improvement in network throughput compared to traditional OSPF routing algorithms because of the measurement of the network throughput. The performance of OSPF is measured similar to that of DQN-EC under a probability of failure of 0-4%. However, from 5% of the probability of a switch failure, the network throughput of DQN-EC and OSPF becomes increasingly different owing to increased network traffic and congestion probability in the network. In OSPF, when the probability of a switch failure is 12%, the measured network throughput of 455 MB/s decreases slightly as the probability of a switch failure increases to 15%.