## 5 Performance Evaluation
### Experimental Results  
#### 5.3.1 The Influence of the Number of Edge Node CPU Cores  
We conducted an in-depth study on the impact of the number of available cores on edge servers on the algorithm results. The number of cores on each edge server is investigated within the range of 5 to 30, with an interval of 1, as shown in Figure 5. The results indicate that as the number of server cores increases, the latency of all three algorithms gradually decreases. This is because the number of available idle cores per server increases, enabling each algorithm to better allocate corresponding cores for incoming tasks, enhancing task efficiency and parallelism, and subsequently reducing computation time and waiting time. Notably, regardless of the variation in the number of server cores, our offloading algorithm consistently exhibits the lowest latency compared to the random algorithm and genetic algorithm. Moreover, Figure 5 demonstrates that as the number of server cores increases, our algorithm maintains a declining trend in average energy consumption, consistently outperforming the other two baseline algorithms. This characteristic underscores the capability of our algorithm to minimize energy usage. Figure 5 also illustrates that our offloading algorithm surpasses the other two baseline algorithms in terms of comprehensive metrics encompassing latency and energy consumption. It can also be seen intuitively from Figure 5 that the strip distribution of multiple experimental results shows that our algorithm always outperforms the baseline algorithm, indicating the consistency of our experimental results. The specific experimental data on latency and energy consumption as the number of Edge Node CPU cores increases are presented in Table 1. The superiority of our algorithm can be specifically seen from the table. From Figure 5, we can see that as the number of cores increases, the energy consumption of our algorithm and baseline algorithm remains basically unchanged. This is because an increase in the number of cores will only reduce the queuing probability and latency of reaching subtasks. In our energy consumption calculation method, only transmission energy consumption and calculation energy consumption are included, and the reduction in queuing delay does not affect the calculation of energy consumption, so the energy consumption remains basically unchanged. For fixed partition granularity, energy consumption decreases as the number ofcores increases. This is because the granularity of the split remains constant, which means that as the number of cores increases, the method cannot achieve optimal partitioning. Figure 5 further compares the performance of our method against a fixed partitioning granularity model for task partitioning. As evident from the figure, when running the same task offloading algorithm, the latency, energy consumption, and comprehensive metrics associated with fixed partitioning granularity are significantly inferior to those achieved by our method. This disparity stems from the fact that our method exhaustively explores various partitioning granularities and ultimately selects the one that optimizes the comprehensive metrics for peak performance. The rationale behind this approach is that excessively large partitioning granularities result in oversized subtasks that fail to fully capitalize on the parallelism benefits of task partitioning, while excessively small granularities increase data redundancy and subsequently expand the task workload. Our method addresses these issues by striking a balance between the two extremes to achieve optimal performance.  
Figure 5: The influence of the number of Edge Node CPU Cores.