# Generative AI-Enabled Energy-Efficient Mobile Augmented Reality in Multi-Access Edge Computing
## 5 Performance Evaluation
### Performance Evaluation  
To evaluate the impact of weight parameters for latency and energy consumption, we first evaluate the change of latency and energy consumption with different weight parameters \(w_{1}\) and \(w_{2}\) in fixed client number \(15\). We identify trends by measuring the latency and energy consumption with the proposed algorithm while changing only as the weight parameters \(w_{1}\) and \(w_{2}\) in the above experimental environment. As shown in Figure 5, the weight parameter for latency \(w_{1}\) increases and the weight parameter for energy consumption \(w_{2}\) decreases, and the computation latency decreases and the energy consumption of MAR clients increases. On the contrary, as \(w_{2}\) increases and \(w_{1}\) decreases, the energy consumption of MAR clients increases, and the computation latency decreases. This means that latency and energy consumption are in a trade-off relationship regarding weight parameters \(w_{1}\) and \(w_{2}\). Since this paper aims to balance the latency, energy consumption of MAR clients, and service satisfaction, we choose weight parameters \(w_{1}=0.5\) and \(w_{2}=0.5\) for performance evaluation. Next, we evaluate the performance of the proposed algorithm and benchmarks in terms of latency, energy consumption, and total cost that we defined with two different SR methods, EDSR and SRGAN. First of all, Figure 6 shows the result of the proposed total cost model from the proposed algorithm and benchmarks with the EDSR SR model. The proposed algorithm achieves the minimum values at the cost function compared to benchmarks. This is because the proposed cost model considered not only latency and energy consumption but also the satisfaction of service quality obtained from video resolution \(Q_{n}\) by balancing all those factors. This is why the proposed algorithm achieves the minimum cost despite the proposed algorithm not gaining the definite minimum value in latency and energy consumption. Finally, the reason why benchmark 4 achieved a higher total cost than our proposed algorithm is that it is difficult to accurately adapt thesatisfaction caused by SR process with a rule-based algorithm. Therefore, we can conclude that optimizing the offloading and SR decision leads to the balance of the computation latency, energy consumption, and service quality. Figures 7 and 8 show the detailed results in terms of latency and energy consumption from the proposed model and benchmarks with the EDSR SR model. As shown in Figure 7, as the number of clients increases, the latency also increases, except for all local computing environments named benchmark 1. The main reason is that the offloading strategies including the proposed algorithm are greatly affected by the available network and computing resources of the MEC server. In other words, as the number of MAR clients increases, the allocated bandwidth and computing resource for each client decreases. However, the latency of benchmark 1 only affects the local computing resource of each client. In Figure 8, we present the energy consumption generated by different offloading and SR strategies. It can be seen that the number of clients does not greatly impact energy consumption. This is because the energy consumption of MAR clients is largely affected by the task computation energy rather than the transmission energy. It means that a client's energy consumption is  
Figure 5: The impact of latency and energy consumption on the change of weight parameters. Figure 6: Total performance of proposed cost model per number of MAR users (MMN clients) with EDSR SR model. mainly determined by their available computing resources. Also, it can be seen that the proposed algorithm shows an almost identical average value as another offloading strategy. This is because all offloading strategies including the proposed algorithm, benchmark 2, and 3 adopt the same binary offloading strategy, and the proportion of task computation energy is larger than transmission energy.