## 4 Performance Evaluation  
In this section, we present experimental results that demonstrate the performance of the LCDA in terms of reducing the processing time by managing task offloading while guaranteeing task deadline satisfaction. The experiments evaluate how different offloading techniques affect the service time of the MEC systems, and analyze the impact on its functions, namely the task failure rate and processing time for different numbers of devices, tasks, task ratios, and execution times. We simulate various scenarios using the EdgeCloudSim simulator [12]. This simulator extends the CloudSim toolkit [13] with additional features that allow networking resources and edge node modeling to enable accurate simulations of the physical edge infrastructure. For the experiments, we assume that there are four VMs and one host running in the central cloud center, 14 hosts, and eight VMs running in the edge cloud center, and 500 devices (users) unless specified otherwise. The user can specify the type of the application such as task length, data transmission speed, and delay sensitivity. To verify the effectiveness of the LCDA, we consider four different kinds of applications; augmented reality, health, heavy computation, and infotainment. The augmented reality and health applications have a short task length, and the increase of CPU utilization of the edge cloud servers is relatively low. However, as it is important to obtain a quick response, we have set these two applications to be latency-sensitive. The heavy computation and infotainment have long task lengths, and the increase of CPU utilization of the edge cloud server is relatively high. Since the response time and accuracy are relatively insignificant for playing games, we set them as latency-tolerant applications. As for the default application setting, we use two latency-sensitive applications and two latency-tolerant applications. The parameters of this simulation are listed in Table 1.  
In this experiment, we conduct a performance analysis by comparing five offloading algorithms, including our LCDA. First, a network-based offloading algorithm performs the task on the edge cloud server and offloads the tasks to the central cloud if the network capacity is exceeded. As mentioned earlier in the paper, it is assumed that the WAN environment serves as the connection between the edge cloud server and the central cloud server. We measure the delay that occurs when sending a 1-Mbit dummy task to the cloud in a WAN environment and then divide the delay value by one to set the bandwidth (Mbps) of the WAN. When the value of this bandwidth exceeds 6 (that is, when the delay time is less than 0.165 s), we offload the task to the cloud; otherwise, we operate the task on the edge cloud server. Second, a utilization-based offloading algorithm performs the task offloading from the edge server to the central cloud server when a certain amount of CPU utilization is exceeded. In this case, the CPU threshold of the edge cloud server is 80%. Third, a hybrid offloading algorithm combines these two cases. Finally, a Mobile+Edge algorithm runs a task on the user device and offloads the task to the edge cloud server when the amount of necessary mobile resources exceeds the amount of available resources. In this case, if the requested CPU capacity of a task exceeds that of the current user device, the task offloads to the edge server otherwise, the execution continues on the current user device. For all those offloading techniques, we evaluate the task failure rates and average processing times, which are important performance criteria in distributed computing. In Figure 4, the x-axis represents the scalability of devices (users), and the y-axis denote the task failure rate and processing time, respectively. Overall, the task failure rate and processing time tend to increase slightly, with an increase in the number of devices. The task failure rate is the lowest for the Mobile+Edge algorithm, and the LCDA has the lowest task failures, except for the Mobile+Edge algorithm.