## 4 Performance Evaluation
To measure the efficiency of the LCDA algorithm from various perspectives, we evaluated its performance by increasing simulation execution time, as shown in Figure 7. The time was measured at intervals of 10, 30, 60, and 120 min. As expected, the performance of all the approaches seems similar to the previous experiments. The Mobile+Edge approach showed the best and worst performers in terms of task failure rate and processing time, respectively. In terms of the task failure rate, the LCDA reduces the task failure rate by 32.8% compared to the other three methods. In terms of processing time, the LCDA shows 83.1% better performance compared to the Mobile+Edge approach. To evaluate the progressing improvement for the latency-sensitive applications, we checked the results of the experiments from two perspectives for each application. Figure 8 shows the results of  
Figure 6: Performance comparison for latency-tolerant to latency-sensitive ratio. Figure 7: Performance comparison with respect to execution time. comparing the performance of the failed task rate for four applications. The LCDA and Mobile+Edge algorithms are both employed by increasing the number of devices from 200 to 1600 for 10 min. From the task failure rate perspective, the overall performance is better with the Mobile+Edge algorithm than the LCDA. However, in terms of accurate processing, the performance of the augmented reality and health applications, which are latency-sensitive applications, outperform those of heavy computation and infotainment applications, which are latency-tolerant, and satisfy our experimental goal (Figure 8a). On the other hand, Figure 8b shows that the task failure rate for the augmented reality applications where fast and accurate execution is most important and is worse than the infotainment, which is latency-tolerant. In Figure 8a, the failed task rate range of the heavy computation application is about 1-5% and has the worst performance compared to the other three applications with about a 0-1.5% failed task rate range. This result shows that even though the LCDA is applied, the performance is low for the application with a long task length. In Figure 8b, there are fluctuations in the failed task rate of the latency-tolerant applications. This result shows that offloading without considering the task type provides unstable performance for latency-tolerant applications. The experimental procedures for the graphs in Figure 9 are the same as those for Figure 8, except that the performance comparison target is the processing time and not task failure rate. When comparing the LCDA and the Mobile+Edge application, the average performance seems to have improved by 70.3% for the LCDA. Even though heavy computation and infotainment applications use a lot of resources than augmented reality and health applications, we have guaranteed short processing time for the latency-sensitive applications in situations where multiple applications are running concurrently. In particular, from observing the processing time for augmented reality, which is a latency-sensitive application, the priority of the augmented reality application over the infotainment application can be guaranteed when the LCDA is applied. Figure 8: Performance comparison of the failed task rate for different application types.