# Joint Optimization of Service Migration and Resource Allocation in Mobile Edge-Cloud Computing
## 5 Performance Evaluation
### Simulation Results
Notably, under tighter time constraints, simple follow-migration methods exhibit higher failure rates, whereas our method consistently outperforms others. With more lenient time constraints, our method maintains a task failure rate below 10%. These observations highlight the importance of intelligently performing dynamic migration and resource allocation according to the current state in dynamic environments. Figure 6: The impact of the time constraint on average response delay. Figure 7: The impact of the time constraint on failure rate. Impact of the number of users:The number of users in the system directly impacts the quantity of tasks, influencing the QoS for users. In this comparison, the number of ESs, task sizes, and time slot durations are consistent with previous experiments. We increment the number of users from 8 to 12, adjusting by one each time. As demonstrated in Figure 8, the average service response delay for all methods increases as the number of users rises due to more intense competition for resources among tasks. Notably, when the user count reaches 11, the average response delays for our method and the PSO approach converge. However, our approach consistently maintains a lower average response delay across various scenarios compared to other benchmarks. Furthermore, as shown in Figure 9, the PSO method requires significantly more time to make migration and resource allocation decisions than other methods, which may be impractical in real-world settings. Our method, however, delivers optimal results more efficiently. Figure 10 illustrates that task failure rates increase with the number of users. Despite this, our method outperforms others even under intense resource competition, achieving failure rates approximately 10% lower than PSO and 30% lower than the Follow-Avg method. Impact of task data size:In this section, we compare the impact of different task sizes on the results by varying the task sizes generated by the applications while keeping the number of ESs, time slot duration, and number of users consistent with the previous experiments. Regarding the task sizes generated by the applications, we set the application data size range from \(2\pm 0.5\) MB to \(3\pm 0.5\) MB, with an increment of 0.25 MB each time. Figure 11 demonstrates the impact of different application data sizes on the decisions made by various methods, with average response delay as the metric. It can be observed that as the task data size increases, the average response delay gradually rises. One reason for this is that the task's data size affects migration delay, thereby reducing the time available for computation. Additionally, although the computational resources in the system remain unchanged, an increase in data size also contributes to an increase in average response delay. Figure 12 illustrates the variation in task failure rates under different data sizes. We observe that as the data size increases, the increase in failure rate becomes more pronounced for the Follow-Avg scheme. In the scenario with the highest data size, the failure rate of the Follow-Avg scheme exceeds that of the PPO and PSO schemes, while our approach ensures the lowest failure rate in all scenarios. Overall, our method achieves lower response delay while maintaining a low failure rate. Figure 11: The impact of data size on average response delay. Figure 10: The impact of the number of users on failure rate. _Impact of network scale expansion:_ To evaluate the effectiveness of the algorithm in a real-world scenario, we increased the number of users to 40 and the number of ESs to 20. As shown in Figure 12(a), our method achieved the best performance in terms of average response delay, reducing it by 0.1 s compared to other strategies. Additionally, Figure 12(b) shows that the average failure rate was reduced by 10% compared to other methods. Overall, in a large-scale network environment, our method outperforms other approaches in dynamic migration and resource allocation strategies, maintaining lower average response delays and failure rates.