# Edge Computing Simulators for IoT System Design: An Analysis of Qualities and Metrics
## 4 Supported Qualities by Edge Computing Simulators  
The use of simulation as a method for analyzing and optimizing IoT systems has been established, for example, for designing efficient networks or for investigating the scalability of a specific system [25]. To this end, a large number of simulators exists which can be used for investigating different aspects of IoT systems. To analyze the distribution and processing of tasks, there are many simulators that focus on aspects of cloud computing [26]. These simulators support, for instance, the design of architectures, virtualization management, or investigations of the system's security. Identifying available simulators for edge computing has been recently investigated by [PERSON] et al. [10], who extracted and explained features of each simulator. Here, we use the same list of simulators (shown in Figure 4) for extracting the supported qualities and metrics. Having features and supporting qualities help IoT systems designers select the appropriate simulator. The supported qualities and metrics by simulators are shown in the Table 1.  
Figure 4: Relations of edge computing simulators. Figure 3: Qualities and related metrics identified in the literature. The iFogSim [18] simulator has been commonly used among the literature for simulating edge computing and mainly aims to provide an evaluation platform for resource management policies. This simulator is based on the CloudSim [21], which has been broadly used for simulating cloud computing. iFogSim provides users with quantitative measurements of energy consumption, latency, and network congestion to quantitatively evaluate the qualities. For the latency, a control loop which contains the processing components can be specified to calculate the processing latency from sending a request to receive a response. For energy consumption, the iFogSim simulator calculates the amount of consumed energy by each device in the system. It is also possible to measure the amount of network used between different devices. Moreover, this simulator measures resource usage of edge and cloud devices to be used in the internal resource allocation algorithm. By focusing on simulation of the network characteristics of distributed edge computing devices, the FogNetSim++ [27] simulator aims to simulate a large network of devices. This simulator is based on the well-known OMNET++ [28] network simulator, which includes extensive network libraries and supports many network protocols. The network model they used is based on the publish-subscribe mechanism and a broker device is responsible to manage other devices. In this model, the measured delay is the time taken from the end device to the broker node. For the energy consumption, residual energy of end devices and also energy consumption for each task is calculated. Execution time for each task, number of requests that can be processed based on the capacity of Edge devices, and the queue waiting time for each request have also been considered by this simulator. Moreover, the packet error rate of the wireless channel is used for internal measurements which was not among the identified qualities. The EdgeCloudSim simulator, similar to the iFogSim simulator, is also an extension of the CloudSim simulator with a special focus on demands from edge computing [19]. In contrast to iFogSim, its focus lies on the more dynamic and realistic investigation of service usage, as well as the consideration of mobility. For this purpose, the EdgeCloudSim simulator implements mobility models which enable the simulation of the movement of mobile devices, and thus the investigation of the rate