## 6 Experimental Study
### The Performance of Static Scheduling
Regarding RLPNet, the original research paper considers multiple edge servers where requests are sent to the nearest server for processing, followed by scheduling the requests on the edge servers. The objective of the method is to optimize resource utilization, average waiting time, and average running time across all edge servers. In this case, the average running time was removed to make the network applicable to proposed problem. It can be observed that all three reinforcement learning-based methods have significant advantages in terms of response time. Among them, NeuRewriter performs the best, followed by proposed method in this paper, and then RLPNet. Since all three reinforcement learning methods primarily focus on optimizing response time or solely optimizing response time, it is expected that resource consumption is higher. This is a reasonable phenomenon because idle resources should not be left unused while there are tasks that can be scheduled. Additionally, the computation time (in milliseconds) of all methods for different sub-task quantities was analyzed. It is important to note that when testing the computation time, only a single dataset was used instead of multiple datasets. This choice was made because rule-based algorithms do not have the capability to perform parallel computation with multiple datasets, and comparing them to neural network-based methods based on a single dataset would naturally result in slower computation. The results are shown in Table 7.  
The experimental results indicate that rule-based algorithms have the shortest computation time. This is because these methods have relatively simple scheduling rules. FCFS and SJF exhibit similar computation time, as they only require simple sorting to obtain the results. On the other hand, HRRN has a longer computation time compared to FCFS and SJF due to its consideration of running time, waiting time, and priority, requiring additional computations to calculate the response ratio before making a selection. Using reinforcement learning methods can achieve good results. Among these methods, methods that employ networks to guide local searches, such as NeuRewriter, exhibit the lowest response time. However, these methods also have relatively longer computation time. In contrast, the proposed method directly generates scheduling results, which may have poor scheduling performance time compared to NeuRewriter, but it has the advantage of shorter computation time. Compared to RLPNet, the proposed method demonstrates \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **[20; 10]** & **[50; 20]** & **[100; 30]** \\ \hline FCFS & 26 & 55 & 112 \\ SJF & 26 & 56 & 111 \\ HRRN & 37 & 77 & 151 \\ NeuRewriter & 60 & 125 & 242 \\ RLPNet & 65 & 132 & 251 \\ TF-WS & 35 & 84 & 181 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The computation speed of different algorithms. significant advantages in response time while only slightly increasing resource utilization. Additionally, although RLPNet also directly generates scheduling results, its use of an RNN limits parallel processing and thus leads to longer computation times.