# Modeling Dew Computing in DISSECT-CF-Fog
## 2 Related Works
### Simulation Approaches  
Investigating and maintaining IoT-Fog-Cloud systems in the real-world could be extremely expensive due to the financial expenses of IoT devices and cloud services. Furthermore, examining various scheduling and offloading algorithms may be time- and energy-consuming. Simulation approaches have become acceptable solutions for such purposes among researchers because they mimic existing systems in realistic manners. They ensure cost-efficient and scalable environments to test and validate new procedures and algorithms, in which results can be used for further modification of the real system. One of the most well-known simulators dedicated to modeling the cooperation of IoT and fog computing is called iFogSim [10], which extends the functionality of CloudSim toward fog computing. With the latest version, device mobility can be simulated, which attracts the need for application service migration as well, because the positions of the end devices change frequently. This can cause increased response times and the required QoS cannot be guaranteed without migrating services to a more suitable provider. Furthermore, fog node clustering and microservice orchestration of application services are also part of the upgraded system. The literature may refer to this extension as iFogSim2. MobFogSim [11] is derived from the original version of iFogSim, supporting user (i.e., device) migration to minimize access delay by triggering the handover of user devices between computing nodes. Mobility may require migration solutions for VMs and containers to mitigate increasing latency and improve QoS. IoTSim-Edge [12], similar to iFogSim, is built upon CloudSim, and it extends its capabilities with IoT-related behavior, including IoT data generation, battery drainage simulation, local and remote IoT data processing, as well as device mobility. EdgeCloudSim [13] relies on CloudSim as well; therefore, it inherited the functionalities of the core simulator. However, it has a new CPU utilization model, device mobility, and edge orchestrator. To the best of our knowledge, DewSim [14] is currently the only simulator that models a Dew computing environment directly. It mainly focuses on simulating mobile device clusters, of which, members are considered the primary computing resource providers. Trace-based battery simulation is also available in the simulator in order to model battery drainage realistically. With different job allocation strategies, jobs are only distributed among battery-dependent devices; moreover, non-battery-dependent devices and detailed physical infrastructure management are not considered at all. [PERSON] et al. [15] introduced an extended version of DewSim, which supports simulating hybrid Dew/edge environments, including non-battery-dependent devices; however, the management of the computing infrastructure is still missing. DISSECT-CF-Fog [16] deals with a detailed IaaS model, including physical and virtual machines, storage, and data center network properties, among others. In general, its components are split into two main parts, physical and virtual layers. The physical parameters describe the physical capabilities of the fog and cloud nodes. The fog extension also provides a comprehensive IoT layer representation, where IoT physical layer components(sensors and actuators) can be represented, and smart devices with different properties can be modeled. In the virtual layer, IoT applications running on the computing node are responsible for processing data. This layer also considers the energy consumption of the entities, IoT device mobility, and pricing schemes of real providers. We present the simulators in Table 1, where we denote the domains (IoT, edge/Dew computing and fog/cloud computing) and the functionalities available in the tools. According to the preliminary analysis and previous studies [16; 17] (which showed that DISSECT-CF-Fog is more scalable, reliable, and faster than iFogSim), in this paper, we chose DISSECT-CF-Fog to extend towards IoT-Dew-Fog systems by modeling Dew computing.