# Traffic-Aware Optimization of Task Offloading and Content
## 5 Experimental Evaluation
### Comparative Experiments
#### 5.2.5 Comparison with Change in Edge Server Computing Power  
With a 50% increase in edge server computational capacity, TOCC demonstrates superior average execution times compared to LOCAL, TFO, and F_NSGA-III, as illustrated in Figure (a)a. LOCAL remains unaffected by changes in the edge server computational capacity, maintaining a consistent average execution time. TFO, on the other hand, experiences a significant impact, resulting in a continuous decrease in average execution time. Specifically, TOCC exhibits a 33% improvement over the LOCAL algorithm, a 27% improvement over TFO, and a 7% improvement over F_NSGA-III in terms of the average execution time. Figure (b)b reveals that as the edge server computational capacity increases, TOCC consistently achieves the lowest total energy consumption relative to LOCAL, TFO, and F_NSGA-III. In terms of the total energy consumption, TOCC surpasses LOCAL by 43%, outperforms TFO by 83%, and surpasses F_NSGA-III by 97%. Interestingly, TOCC does not exhibit the significant latency advantage we initially anticipated with an increasing edge server computational capacity; instead, it fluctuates within a certain range. Combining these findings with multiple experimental datasets, we can ascertain that, according to Equations (3) and (4), an increase in the edge server computational capacity should result in a rapid increase in energy consumption. However, TOCC strikes a balance between energy consumption and latency, preventing excessive energy consumption at the cost of sacrificing some latency.  
Figure 8: Comparison of average execution time and total energy consumption with change in edge server computing power.  
Figure 7: Comparison of average execution time and total energy consumption with change in edge server storage capacity.  
In our research, the TOCC algorithm is an effective means to solve joint optimization problems through multi-objective optimization. However, we encountered some challenges when exploring the normalization problem of latency and energy consumption. The original idea is to normalize the delay and the energy consumption and convert them into a single-objective problem to solve through objective weighting. However, during the experiment, we faced some difficulties. First, the dimensions of delay and energy consumption were inconsistent, making it difficult to find a suitable ratio in the fitness function, which makes the function too exaggerated. Although we observed in our experiments that there seems to be a quasi-linear relationship between latency and energy consumption, the problem of dimensional inconsistency in the fitness function still exists. Secondly, since we have not discussed specific business scenarios in detail, the importance of latency and energy consumption is uncertain in practical applications. The TOCC algorithm excels in this regard because it is able to complete trade-offs within the algorithm itself without the need for detailed discussions of specific business scenarios. This enables the TOCC algorithm to effectively achieve the goal of joint optimization without being affected by dimensional inconsistencies in the delay and the energy consumption and uncertainty in business scenarios. Therefore, the TOCC algorithm shows a strong robustness and adaptability in solving joint optimization problems.