# An Approach for Deployment of Service-Oriented Simulation Run-Time Resources
## 6 Experiment Evaluation
### Experiment Results
#### 6.4.1 Optimality and Scalability  
Figures 3-5 present the two objective function values of solutions in the highest Pareto rank generated by the mentioned six algorithms in the upper half when the number of micro-services is 10, 35, and 40, respectively. They also illustrate the iteration process of three evolutionary algorithms, including KMNSGA, NSGA-II, and LGASRD, in the lower half. Due to the fact that some parameters related to resources and user demand were randomly generated for each run, TServiceD and TSD2 obtained different results even though they have specific deployment rules. In these figures, we present the results of LGASRD in two forms: the normal population and the elite population, represented by red and green stars, respectively. Note that since the solutions obtained by LGASRD are too dense, they appear as a single point in the figures. Therefore, we enlarged the points representing the results calculated by LGASRD and placed them in a black box in the blank area. As can be observed from Figures 3-5 and Table 5, LGASRD exhibits the best comprehensive performance among the six algorithms tested. In experiments conducted with settings A, C, and D, both two objective functions achieved minimal values by LGASRD. This fully demonstrates the effectiveness of the strategies we used, such as elite knowledge and OPK, in enhancing the performance of NSGA-II. Under other experimental settings, LGASRD always obtained solutions that were not dominated by those of other algorithms, with a minimum value for a specific objective function. Figure 4: Algorithm performance comparison and iteration process of three evolutionary algorithms under experimental settings D, E and F.  
Figure 3: Algorithm performance comparison and iteration process of three evolutionary algorithms under experimental settings A, B and C.  
Under these experimental settings, the performance of two other evolutionary algorithms, including KMNSGA and NSGA-II, and TServiceD, was also good and they sometimes obtained smaller values on another objective function than LGASRD. The reason for this may be that when the scale of experiments increased, LGASRD reached the termination condition before fully converging, failing to obtain better solutions. It can also be observed that Random, TServiceD, and TSD2 often produce unfeasible solutions. This may be due to their failure to consider the resources required by the simulation middleware and constructive members when using heuristic deployment rules for micro-services, thus directing a large amount of resources towards the deployment of micro-services. Under three experimental settings where the number of micro-services is consistent, i.e., experiments presented in Figures 3-5, as the number of edge servers and simulation members increases, the time cost within the system also increases. This is normal, as an increase in the number of elements within the simulation system will inevitably result in an increase in the scale and frequency of requests for services and data transmission. In order to explore this in more detail, we summarize how the time costs of Pareto optimal solutions obtained by three evolutionary algorithms change as the scale of our experiment varies, as shown in Table 6. The experimental scale mentioned here mainly refers to the product of the number of users and edge servers, as well as the number of micro-services. In Table 6, they are presented in the left half and the right half, respectively. To provide a visual representation, we have only provided ratios of time costs under  
Figure 5: Algorithm performance comparison and iteration process of three evolutionary algorithms under experimental settings G, H and I.