## 5 Simulation Result and Discussion
### Simulation Based on Congestion Condition  
Congestion in mobile edge computing occurs when the network or resources in the infrastructure are overloaded with a high volume of data or requests. This overload leads to delays, increased latency, and degraded performance for applications and services. Congestion can result from factors like high user demand, limited network bandwidth, inadequate processing capacity, or improper resource allocation. Effective management of congestion is essential to maintain the efficient and reliable operation of mobile edge computing systems. By implementing strategies like Load Balancing, resource provisioning, and traffic prioritization, congestion can be alleviated, and the overall performance of mobile edge computing can be improved. However, our proposed approach based on Lagrange can perform better in congestion conditions. Figure 5: Total latency and cost based on different numbers of MEC servers. In Figure 6 on the left side, we compare the performance of total latency on average in terms of different congestion conditions. The figure shows the total latency in seconds obtained by each approach. The proposed Lagrange approach achieves total latencies between 0.02 and 0.06 s under the no-congestion condition. Next, in low-congestion conditions, it can achieve a total latency between 2.56 and 3.20 s. In moderate congestion, the total latency is 32.00 s, and finally it is 64.00 s in high-congestion conditions. These results demonstrate that even in conditions of high congestion, the proposed algorithm still performs well in reducing latency. In contrast, the conventional approaches show different results: Greedy Latency achieves total latencies between 0.08 and 0.20 s under the no-congestion condition. Next, in low-congestion conditions, it can obtain a total latency between 7.91 and 9.89 s. In moderate congestion, the total latency is 98.89 s, and finally it is 197.79 s in high-congestion conditions. Load Balancing can obtain total latencies between 0.08 and 0.21 s under the no-congestion condition. Next, in low-congestion conditions, it can obtain a total latency between 8.58 and 10.73 s. In moderate congestion, the total latency is 107.31 s, and it is 214.62 s in high-congestion conditions. Finally, Random Offloading has a total latency between 0.09 and 0.24 s for the no-congestion condition. Next, in low-congestion conditions, it can obtain a total latency between 9.46 and 11.83 s. In moderate congestion, the total latency is 118.29 s, and finally it is 236.58 s in the high-congestion condition. If we compare the performance of the total latency on average in terms of all congestion conditions, the total latency of our proposed approach is 209.07% better than that of Greedy Latency, 235.36% better than that of Load Balancing, and 269.69% better than that of Random Offloading scheme. In Figure 6 on the right side, we compare the performance of total cost on average in terms of different congestion conditions. This figure presents a comparison of total cost between the proposed Lagrange approach and three conventional task offloading schemes in congestion conditions such as no congestion, low congestion, moderate congestion, and high congestion. The figure shows the total cost obtained by each approach. The proposed Lagrange approach achieves a total cost between 0.0255 and 0.0720 under no-congestion conditions. Next, in low-congestion conditions, it can achieve a total cost between 2.8800 and 3.6000. In moderate congestion, the cost is 36.0000, and finally it is 72.0000 in high-congestion conditions. These results demonstrate that even in conditions of high congestion, the proposed algorithm still performs well in reducing computation costs. In contrast, the conventional approaches show different results: Greedy Latency achieves a total cost between 0.0934 and 0.02368 under the no-congestion condition. Next, in low-congestion conditions, it can obtain a total cost between 9.4721 and 11.8401. In  
Figure 6: Total latency and cost based on congestion conditions.