# An Approach for Deployment of Service-Oriented Simulation Run-Time Resources
## 6 Experiment Evaluation
### Experiment Results
#### 6.4.1 Optimality and Scalability
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Best on O1** & **Better than LGASRD on O1** & **Best on O2** & **Better than LGASRD on O2** \\ \hline KMNSGA & 0 & 0 & 3 & 3 \\ NSGA-II & 0 & 1 & 1 & 2 \\ LGASRD & 7 & & 5 & \\ Random & 2 & 2 & 0 & 0 \\ TServiceD & 0 & 0 & 0 & 0 \\ TSD\({}^{2}\) & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of mentioned algorithms on two objective functions across nine experimental settings. different scales without providing specific numerical values. We are only interested in the impact of the experiment scale on time cost and not comparing the performance of the mentioned algorithms, so the results for each algorithm are independent. Due to the limited data, it is difficult to quantitatively describe the growth trend of time cost with experimental scale increases. However, it is evident that the increase in time cost caused by the increase in the number of micro-services is significant, and the underlying reasons still need to be explored. On the contrary, the impact of the number of edge servers and users on time cost is relatively small, even though when the quantity of edge servers and simulation users increases, the increment of time cost exceeds the increase in experimental scale. From the lower part of Figures 3-5, it can be observed that, unlike KMNSGA and NSGA-II, LGASRD exhibits significant fluctuations in its iteration curve. After reaching a local minimum, LGASRD often continues to reduce the objective function value through guided by EP and OPK. This indicates that LGASRD possesses a stronger ability to escape local optimal solutions compared to the other two evolutionary algorithms, which is one of the main reasons for its superior performance. Furthermore, compared to KMNSGA and NSGA-II, LGASRD also exhibits a faster convergence speed. When EP fails to update consecutively for multiple generations, the algorithm terminates even if the normal population is still becoming better. We also conducted experiments with settings J, K, and L, and the results are shown in Figure 6. It can be observed that LGASRD still exhibits excellent optimization performance, with its solutions dominating those generated by other comparison algorithms, except for slightly higher time cost compared to the solutions produced by TServiceD and TSD\({}^{2}\) in Figure 6b. Furthermore, if we compare the numerical values of time cost in Figures 3 and 6, we will find that if other settings are the same, the granularity of service requests does not have a significant impact on the magnitude of the training simulation system's time cost. The granularity of a service request refers to the number of micro-services involved in it, i.e., the number of tasks in a workflow application. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Number of Edge Servers and Users**} & \multicolumn{6}{c}{**Number of Micro-Services**} \\
**Setting** & **Scale** & **KMNSGA** & **NSGA-II** & **LGASRD** & **Setting** & **Scale** & **KMNSGA** & **NSGA-II** & **LGASRD** \\ \hline A & 1 & 1 & 1 & 1 & A & 1 & 1 & 1 \\ B & 4.5 & 8.17 & 5.76 & 10.40 & D & 3.5 & 1897.87 & 2052.43 & 1517.05 \\ C & 10 & 37.14 & 22.62 & 29.66 & G & 4 & 2378.72 & 2059.93 & 2238.64 \\ \hline D & 1 & 1 & 1 & 1 & B & 1 & 1 & 1 \\ E & 4.5 & 7.87 & 6.77 & 10.07 & E & 3.5 & 1828.13 & 2409.09 & 1469.95 \\ F & 10 & 32.74 & 18.98 & 34.27 & H & 4 & 1505.21 & 1103.90 & 890.71 \\ \hline G & 1 & 1 & 1 & 1 & C & 1 & 1 & 1 & 1 \\ H & 4.5 & 5.17 & 3.09 & 4.14 & F & 3.5 & 1672.39 & 1721.85 & 1752.87 \\ I & 10 & 12.74 & 9.13 & 11.19 & I & 4 & 815.58 & 831.16 & 844.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Changes in Time Cost with the Increase in the Number of Edge Servers, Users, and Micro-services.