## 6 Experimental Study
### Performance of DRL Method to Network
#### 6.3.2 The Effect of a Neighborhood-Based Parameter-Transfer Strategy  
To evaluate the effectiveness of the parameter transfer strategy, a set of comparative experiments was conducted. By using the neighborhood-based parameter transfer strategy, networks for different reward weight combinations could be trained quickly. With this method, there was no longer a need to initialize the network parameters and train it for 100 epochs. Instead, parameters were transferred from a previously trained network for a neighboring sub-problem, significantly reducing the training time of the network.  
Table 3 demonstrates the results of the first epoch when training with the \((\alpha,\beta)=(0.5,1)\) weight combination on the [20; 10] dataset. A comparison was made between using and not using the parameter-transfer strategy. It can be observed that using the parameter-transfer strategy resulted in better performance in the first epoch.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Target Set of** & **Original Set of** & **reward** & **reward\({}_{1}\)** & **reward\({}_{2}\)** \\
**Weights** & **Weights** & **reward** & **reward\({}_{1}\)** & **reward\({}_{2}\)** \\ \hline \((0.5,1)\) & null & 0.572 & 0.41102 & 0.367 \\ \((0.5,1)\) & (1, 0.5) & 0.505 & 0.41140 & 0.299 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effect of using and not using neighborhood-based parameter-transfer strategy.  
Figure 6: Convergence curves of different training methods.  
Priority Function Analysis  
The priority function \(f(P_{j}^{i})\) is simply set as a linear function (other functions can also be used), expressed as \(f(P_{j}^{i})=k_{1}\times p_{j}^{i}+b\). The effect of the coefficient \(k_{1}\) on the network is analyzed. The constant \(b\) is fixed at 1, and the tests are conducted using the [20; 10] dataset. The experimental results are presented in Table 5.  
It was observed that as \(k_{1}\) increases, \(reward_{2}\) exhibits larger variations. Based on the previous analysis, it is known that as the proportion of \(reward_{2}\) increases, the model tends to prioritize optimizing \(reward_{2}\), resulting in poorer optimization of \(reward_{1}\). Therefore, it can be concluded that an increase in \(k_{1}\) leads to the model paying more attention to tasks with higher scheduling priorities, causing longer waiting times for sub-tasks with lower priorities and higher resource utilization. The coefficient \(k_{1}=0.1\) was selected for subsequent experiments. However, in practical usage, the value of \(k_{1}\) can be adjusted based on specific requirements and conditions.  
Penalty Analysis  
Concerning Penalty, sub-tasks surpassing \(k_{2}\) times the average response time (\(t_{avg\_resp}\)) are classified as timeout sub-tasks. The value of \(k_{2}\) can be adjusted based on the urgency of jobs in the specific context. Here, \(k_{2}\) is set to 3 (adjustable according to the actual requirements). By varying the magnitude of the Penalty, the analysis examines its impact on \(reward_{2}\) and the number of timeout sub-tasks.  
From the results shown in Table 6, it can be observed that setting a low value for the Penalty renders it ineffective. On the other hand, setting a high value for the Penalty significantly affects the value of \(reward_{2}\), leading to significant fluctuations in the reward during training. Additionally, if the value of the Penalty remains constant and independent of the sub-task quantity, the number of timeout tasks will inevitably increase as the task workload grows. This would have a significant impact on the value of \(reward_{2}\), making it challenging for the network to converge.  
Therefore, it can be concluded that by binding the quantity of the Penalty to the input sub-task, allowing it to vary with changes in the action space, can effectively reduce the occurrence of timeout sub-tasks and mitigate the fluctuations in the value of \(reward_{2}\).