## 6 Experiment Results
### Experimental Results
#### 6.2.2 Performance Metrics of the PER-iSAC Model  
In the experiment comparing the average completion times of the algorithms as task size increases, six task sizes were designed: 20 MB, 50 MB, 80 MB, 120 MB, 150 MB, and 180 MB. Each algorithm performed 10,000 task offloads for each task size. Figure 6 presents a comparison of the average task completion times for the three methods under varying task sizes. As the task size increases, the average completion time of the three algorithms all increases. When the task size is small (20 MB to 80 MB), the average completion time of the algorithms is relatively low and grows slowly. When the task size reaches 80 MB, the average completion times of PPO Baseline and Standard SAC begin to increase significantly, while the growth  
Figure 4: Training convergence plots.  
rate of PER-iSAC is relatively small. When the task size reaches 150-180 MB, the average completion time of PPO Baseline increases the fastest, approaching 500 ms, while the average completion times of PER-iSAC and Standard SAC are about 80 ms and 180 ms, respectively. Under smaller task sizes, there is little difference in the performance of the three algorithms. As the task size increases, the performance of PPO Baseline decreases the fastest and the increase in completion time is the most significant. PER-iSAC shows the best performance across all task sizes, with the lowest average completion time.  
In order to evaluate the upper-bound performance of the proposed model, in this experiment, the task arrival rate was increased to 100,000 tasks per second, which is ten times higher than the training phase setting. Under the high-intensity configuration, the system processed a total of 5000 tasks. As shown in Figure 7a,b, at the beginning of the experiment, as tasks were offloaded to the server, the server's computing power and storage resource utilization rates gradually increased. When 3000 tasks were offloaded, the storage resource utilization rates of the server approached 100%. When 4000 tasks were offloaded, as shown in Figure 7c, the offloading error rate began to occur. The reason for the error was insufficient storage space on the server. Due to memory capacity limitations, once the memory was saturated, no more tasks could be allocated to the server, so the  
Figure 5: Server selection frequency statistics.  
Figure 6: Average completion time for different task sizes.  
average computing power utilization rate remained below 60%. When all 5000 tasks were offloaded, the error rate was approximately 0.8%. Figure 7 presents the energy consumption of edge servers during the same test. The energy usage curve grows steadily and reflects the linear relationship between server load and power consumption, as defined in Equations (5) and (6). These results demonstrate that the proposed algorithm maintains high system efficiency in terms of resource utilization, error rate, and energy consumption control under high-volume task inflow.  
Overall, the PER-iSAC model exhibits robust task-handling capability under high load conditions, maintaining system stability, a bounded failure rate, and energy-efficient operation.