## 4 Simulation Results
The proposed algorithm demonstrates improved throughput with an increasing number of serving MECSs. This improvement is attributed to the ability to partition tasks across a larger number of MECSs, enabling parallel processing. Consequently, the system can handle a higher volume of tasks within the delay constraints, resulting in increased throughput. _Random_ scheme achieves the highest throughput among the compared methods. This result stems from the uniform distribution of tasks across the MECSs, which, despite lacking server load awareness or partitioning strategies, effectively balances the load. In contrast, the _No Partition_ scheme records the lowest throughput when the number of serving MECSs reaches 10. This is because tasks are always assigned to the nearest MECS, increasing the likelihood of exceeding the processing delay threshold, which negatively impacts performance. Unlike the proposed algorithm, the throughput of the _No Partition_ scheme is inconsistent regardless of the number of serving MECSs. This inconsistency is influenced by the movement patterns of HSTs. Interestingly, the proposed algorithm achieves its highest throughput when the number of serving MECSs is 8. This result can be attributed to the even distribution of HSTs, which ensures that tasks are uniformly allocated to MECSs, maximizing processing efficiency and throughput under this configuration. Figure 4b illustrates the performance of throughput as the task arrival rate increases. For this experiment, the number of serving MEC servers was fixed at 10 to evaluate the effectiveness of task distribution across servers. In the case of _No Partition_, the throughput significantly decreases as the task arrival rate increases. This is because, as more tasks are generated, the size of tasks sent to a single MEC server increases, which leads to fewer tasks being processed within the required delay constraints. Notably, at a task arrival rate of 50, no tasks are successfully processed, resulting in a throughput of zero, as indicated by the absence of the corresponding bar in the graph. On the other hand, _Random_ achieves higher throughput as the task arrival rate increases, owing to its ability to distribute tasks evenly across servers. However, the Proposed Method demonstrates the highest throughput at a task arrival rate of 30. This is because, while the proposed method effectively manages task partitioning, the higher task arrival rate leads to an increased number of tasks to be processed, which limits its performance beyond a certain threshold. \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Batch size (\(N\)) & 256 \\ Actor network learning rate & 0.0005 \\ Critic network learning rate & 0.005 \\ Policy update frequency (\(d\)) & 2 \\ Tau (\(\tau\)) & 0.05 \\ Discount rate (\(\gamma\)) & 0.98 \\ Optimizer & Adam \\ \hline \end{tabular}
\end{table} 
Table 2: The hyper parameter of the proposed algorithm. Figure 5 illustrates the performance of average delay under varying task arrival rates and the number of servers. Figure 5a demonstrates that the average delay decreases as the number of servers increases. This improvement can be attributed to the increased availability of serving MEC servers, enabling tasks to be partitioned and processed in parallel. For the _No partition_ approach, tasks are not divided among servers, resulting in significant delays due to the high dependency on the movement of high-speed trains (HSTs). While there is a slight improvement in delay when the number of servers increases from 5 to 8, the overall delay remains the highest compared to other methods since tasks are processed on a single MEC server. In contrast, both the proposed method and the _Random_ approach leverage task partitioning, leading to progressively reduced delays as the number of MEC servers increases. However, the _Random_ method outperforms the proposed method in terms of delay.