# Edge Computing Simulators for IoT System Design: An Analysis of Qualities and Metrics
## 2 Related Work  
The idea of using processing and storage resources at the \"edge\" of an IoT system and providing the required capabilities closer to the source of data has been proposed recently [4]. In some works, the term \"fog computing\" is also used to refer to systems where edge devices carry out a substantial amount of computation and storage. In this paper, we use the term \"edge computing\" to include any computing and storage resources (including fog computing) along the path between sensors or actuators and cloud servers. As mentioned, edge computing performs well as compared with cloud computing with respect to several IoT system QCs such as latency, energy consumption, privacy, and bandwidth. The benefits of edge computing have been investigated in different applications such as deep learning [12] and multimedia delivery [13]. However, cloud computing has many strengths due to its huge processing and storage capacity. To combine the strengths of edge and cloud computing, hybrid edge-cloud approaches have been proposed [14]. The software component deployment problem in a hybrid edge-cloud approach has been investigated in a number of studies. For instance, [PERSON] et al. [15] focused on a comparison between edge and cloud computing by considering generalized models of delay, power consumption, and cost to evaluate performance of IoT applications with and without the existence of edge computing. On the basis of a simple simulation setup, it was concluded that edge computing reduced energy consumption, latency, and cost. Similarly, [PERSON] et al. [5] studied the performance of hybrid edge-cloud architectures based on latency and energy consumption. However, the tradeoffs with conflicting QCs were not considered in these studies.  
Computer simulation has been proven to be a suitable technique to use for investigating different aspects of complex systems, such as service configuration, resource placement, or management strategies in IoT systems. To this end, several simulators (simulation frameworks) were proposed that address specific requirements for the application in cloud computing, for example, modelling of physical networks and virtual topologies [16] or emulating IoT environments [17]. Additionally, there are a few simulators designed for the simulation of edge and cloud computing, for example, iFogSim [18], EdgeCloudSim [19], and IOTSim [20]. Many edge computing simulators are extensions of the comprehensive CloudSim simulator [21] which focuses on the simulation of cloud computing infrastructures. [PERSON] et al. [11] conducted a survey study to identify and analyze edge computing simulators in terms of implemented features such as mobility, scalability, as well as infrastructure and network level modeling, however, they did not study the support of QCs in the simulators. To investigate and compare the performance of different IoT architectures by means of simulation, and in terms of QCs relevant for the application at hand, quantitative metrics must be defined. As outputs, most frameworks focus on general network theoretical metrics, such as latency (end-to-end response time), network congestion, or energy consumption [11; 17]. To investigate and assess edge-cloud architectures, as well as provide design decision support, the application of such standard metrics is not sufficient. For many IoT systems, the QCs required to adequately support and assess design decisions include, for example, accuracy, availability, and privacy. Existing frameworks, however, neither allow for capturing these QCs nor provide functionalities for specifying their own metrics.