# Modeling Dew Computing in DISSECT-CF-Fog
## 4 Evaluation  
### Weather Forecasting Scenario  
In this subsection, we present an IoT-assisted weather forecasting scenario utilizing both fog nodes and Dew services running on Dew devices. As can be sen in Figure 3, in the central European area, drones fly and collect information about temperature, humidity, and wind in the atmosphere (Droneblog.com (visited on 15 June 2022): [[https://www.droneblog.com/how-drones-are-helping-with-weather-forecasting/](https://www.droneblog.com/how-drones-are-helping-with-weather-forecasting/)]([https://www.droneblog.com/how-drones-are-helping-with-weather-forecasting/](https://www.droneblog.com/how-drones-are-helping-with-weather-forecasting/))), the size of the whole area is around 850 \(\times\) 230 km\({}^{2}\). At the beginning, the devices are located at the edge of the map (uniformly distributed) and are heading to opposite sides. Such devices are equipped with computing and storing units (8 CPU cores, 8 GB RAMs, 16 SD-card), and fly at an average speed of 70 km/h. In the case of smart device data transmission, the communication delays are calculated based on average 4G latency (50 ms) weighted with the physical distance to the node utilized by the device. To be as realistic as possible, we applied the cloud schema of the ELKH cloud of ELKH SZTAKI (ELKH cloud of ELKH SZTAKI (visited on 20 May 2022): [[https://science-cloud.hu/en](https://science-cloud.hu/en)]([https://science-cloud.hu/en](https://science-cloud.hu/en))) to determine the CPU processing power, network operations, and storage capacities of physical machines for the experiments. Figure 3 depicts five fog nodes located in Lille, Cologne, Frankfurt, Nuremberg, and Prague, each of them dealing with 32 CPU cores and 64 GB memory. The simulator can also calculate resource utilization costs, so we set VM prices according to the pricing scheme of Amazon Web Services (Amazon Web Service (visited on 15 June 2022): [[https://aws.amazon.com/ec2/pricing/on-demand/](https://aws.amazon.com/ec2/pricing/on-demand/)]([https://aws.amazon.com/ec2/pricing/on-demand/](https://aws.amazon.com/ec2/pricing/on-demand/))). Each VM that runs on fog nodes has 4 CPU cores and 8 GB memory, which costs USD 0.116 hourly (\"_a1.xlarge_\"). To evaluate the proposed proactive service migration strategy, the following use cases are defined: (i) without motion prediction and (ii) with motion prediction using the two-order Markov model. In the first case, this means that service migration only happens when the current position of a device is not covered by any node (i.e., reactive migration); therefore, VMs are not initialized in advance. Our experiments considered a fixed number of drones (i.e., 5000), but with different sizes of node ranges (50 and 100 km), to measure the capacities and limitations of the defined architecture. The devices were moving around for 12 h, and the data sampling period was set to 1 min. A measurement is equivalent to 100 bytes of data generation. For this scenario, we used a PC with Intel Core i5-4460 3.2 GHz, 16 GB RAM, and a 64-bit Windows 10 operating system to run the simulations; due to the random generator of the simulator, we counted the average values of the experiments, repeated five times. To compare the results of the scenario runs defined in the previous section, we measured the following metrics in the simulator:  
* _No. VMs_: Total number of VMs running on computing nodes. * _Node cost_ (_USD_): Total cost of the VMs with application of the AWS pricing. * _Node energy_ (_kWh_): Energy consumption of the computing nodes based on the LPDS cloud. * _Timeout_ (_minute_): The time interval between the last data-unit procured and processed, the shorter this interval is, the more real-time the simulation is. * _Data generated_ (_MB_): The total sizes of the generated data by smart devices. * _Data processed by device_ (_MB_): The total sizes of the data that were processed on the device. * _Device processing time_ (_minutes_): The average processing time of the devices. * _No.