# Edge Computing Simulators for IoT System Design: An Analysis of Qualities and Metrics
## 4 Supported Qualities by Edge Computing Simulators
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & **iFogSim** & **FogNetSim++** & **EdgeCloudSim** & **iGTSim** & **FogTorch II** & **EmuFog** & **FogBed** \\ \hline \multirow{6}{*}{\begin{tabular}{c} Time \\ behavior \\ \end{tabular} } & \begin{tabular}{c} Response \\ time \\ \end{tabular} & Y & - & Y & - & Y & Y & Y \\ \cline{2-8}  & \begin{tabular}{c} Processing \\ delay \\ \end{tabular} & Y & Y & Y & Y & - & - & - \\ \cline{2-8}  & \begin{tabular}{c} Network \\ delay \\ \end{tabular} & - & Y & Y & - & Y & Y & - \\ \cline{2-8}  & \begin{tabular}{c} Storage \\ delay \\ \end{tabular} & - & - & - & - & - & - & - \\ \cline{2-8}  & \begin{tabular}{c} Throughput \\ utilization \\ \end{tabular} & Y & - & Y & Y & Y & - & - \\ \cline{2-8}  & \begin{tabular}{c} Processing \\ utilization \\ \end{tabular} & Y & - & - & Y & - & - & - \\ \cline{2-8}  & \begin{tabular}{c} Memory \\ utilization \\ \end{tabular} & - & - & - & - & Y & - & - \\ \cline{2-8}  & \begin{tabular}{c} IO devices \\ utilization \\ \end{tabular} & - & - & - & - & Y & - & - \\ \cline{2-8}  & \begin{tabular}{c} Energy \\ consumption \\ \end{tabular} & Y & Y & - & - & - & - & - \\ \hline \multirow{3}{*}{Capacity} & \begin{tabular}{c} Transaction \\ processing \\ capacity \\ \end{tabular} & - & Y & Y & - & - & - & - \\ \hline \multirow{2}{*}{Availability} & \begin{tabular}{c} System \\ availability \\ \end{tabular} & - & - & - & - & - & - & - \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Fault \\ tolerance \\ \end{tabular} } &
\begin{tabular}{c} Failure \\ avoidance \\ \end{tabular} & - & - & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Supported qualities and metrics by edge computing simulators. of failed tasks. To overcome mobility-related issues, virtual machine (VM) migration methods can be used, which are not yet supported by EdgeCloudSim. Another special feature of this simulator is the consideration of the effect of network load and transmission delays on the delay of service requests. Finally, a load generator is provided, which allows for the dynamic generation of tasks with individual data size and length. The IoTSim simulator is also built on CloudSim and focuses on the investigation of big data processing [20]. It extends CloudSim's capabilities for modeling IoT applications and facilitates the model building process. Moreover, IoTSim makes use of MapReduce and other big data systems to allow for the processing of large amounts of sensor data. The IoTSim simulator places special emphasis on the simulation of network communication and delays between data storage and processing VMs. This makes IoTSim a well-suited simulator for the investigation of the performance of large IoT applications that generate and process huge amounts of data. Accordingly, the provided measurements are related to MapReduce and VM functions such as execution time of Mapreduce jobs, discrepancy between start of reduce and map tasks, network cost of Mapreduce, and CPU computation cost incurred by VMs. [PERSON] et al. [7] proposed a simulation tool called FogTorchII with the aim of helping IT designers regarding where to deploy each component of an IoT application in a cloud-fog model. They considered two metrics, latency and bandwidth usage, where latency is the service latency and the communication link latency and bandwidth is measured by the link upload and download bandwidth. To calculate the final results of appropriate solutions, they also considered fog resource consumption through the average percentage of consumed RAM and storage. To support repeatable and controllable experiments with real applications, EmuFog [29] proposed an emulation framework for emulating network topologies. This tool was developed on top of MaxiNet [30] network emulator. For quality measurement, they measured latency that is the latency between client and fog node. They also discussed the possibility of using network bandwidth, which they did not consider in the framework.