## 4 Performance Evaluation
Since the LCDA offloads a latency-tolerant task with large size and length preferentially, the failure rate of the LCDA is slightly higher than that of the Mobile+Edge algorithm. On the other hand, the processing time is the worst for the Mobile+Edge algorithm across all algorithms, and the LCDA shows relatively good results. This implies that the LCDA is a sufficient alternative to the existing MEC offloading techniques, which are likely to violate the deadline of a task because of the long processing time due to offloading. Even though the task failure rate of the LCDA is slightly higher than the others, the costs are negligible. To verify the effectiveness of scalability in terms of the number of applications, we conducted another experiment showing the percentage of failed tasks and processing time with respect to the number of applications, as shown in Figure 5. Note that in this experiment, even though the number of applications increases, the ratio of latency-sensitive to latency-tolerant applications is always 5:5 (the number 8 in the x-axis means that the numbers of latency-sensitive and latency-tolerant applications are both 4, and the number 20 in the x-axis means that the numbers of latency-sensitive and latency-tolerant applications are each 10). In terms of the task failure rate, the Mobile+Edge algorithm shows the best performance, and when compared to the other algorithms, the LCDA shows 35.3% better performance on average. In terms of the processing time, the Mobile+Edge algorithm has the worst performance, and the network-based algorithm has the best performance. The LCDA \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Augmented** & **Health** &
\begin{tabular}{c} **Heavy** \\ **Computation** \\ \end{tabular} & **Infotainment** \\ \hline active\_period (sec) & 40 & 45 & 60 & 30 \\ idle\_period (sec) & 20 & 90 & 120 & 45 \\ avg\_data\_upload (KB) & 1500 & 20 & 2500 & 25 \\ avg\_data\_download (KB) & 25 & 1250 & 200 & 1000 \\ avg\_task\_length (MI) & 9000 & 3000 & 45000 & 15000 \\ utilization\_on\_edge [0–100] & 6 & 2 & 30 & 10 \\ utilization\_on\_cloud [0–100] & 0.6 & 0.2 & 3 & 1 \\ delay\_sensitivity [0, 1] & 0.9 & 0.7 & 0.1 & 0.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Application parameter setting in the simulation. shows the second-best performance and consumes approximately 83.3% less time on average than the Mobile+Edge algorithm. In Figure 6, we conducted an experiment to investigate the performance change of the MEC system by varying the ratio of application types. To validate the effect of the ratio of latency-sensitive to latency-tolerant applications, we performed experiments by varying the ratio of the application types from 10:0 to 0:10. The application tagged as latency-sensitive is the augmented reality application, and the application tagged as latency-tolerance is the infotainment application. For instance, if the ratio of the application is 3:7, the experiment is performed for the ratio of three augmented reality applications and seven infotainment applications. Since the latency-tolerant applications are relatively heavy, it can be seen that both task failure rate and processing time of the latency-tolerant tasks are higher than those of the latency-sensitive tasks. Similar to other experiments, the Mobile+Edge  
Figure 4: Scalability based on the number of users (devices). LCDA stands for latency classification deadline aware task offloading algorithm. Figure 5: Performance comparison for the number of applications. technique shows the best performance in terms of task failure rate, and the LCDA has about 43.3% lesser failure rate than those of the other three algorithms. In terms of the processing time, the Mobile+Edge technique shows the worst performance, and the LCDA shows a performance improvement of 77.9% compared to the performance of the Mobile+Edge technique.