# Modeling Dew Computing in DISSECT-CF-Fog
## 3 The Proposed Model for Dew Computing
### Service Migration  
Moving IoT devices may switch service providers at any time in order to ensure the best QoS, which is called mobility-induced service migration. The main issue is how to seamlessly redirect users to other computing notes without interrupting the service, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Simulators** & \multicolumn{3}{c}{**Internet of Things**} & \multicolumn{3}{c}{**Edge/Dew Computing**} & \multicolumn{3}{c}{**Fog/Cloud Computing**} \\ \hline \multirow{2}{*}{-} & \multirow{2}{*}{**IoT Devices**} &
\begin{tabular}{c} **Battery/Energy** \\ **Consumption** \\ \end{tabular} & **Mobility** & **-** & **IaaS** & **Migration** \\ \hline iFogSim & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline MobFogSim & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline IoTSim-Edge & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\chi\) \\ \hline EdgeCloudSim & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\chi\) \\ \hline DewSim & \(\surd\) & \(\surd\) & \(\chi\) & \(\surd\) & \(\chi\) & \(\chi\) \\ \hline DISSECT-CF-Fog & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the related simulation approaches (\(\surd\) means the simulator contains that functionality). especially in the case of time-sensitive applications. Service migration can be distinguished based on timing [18]. In case of proactive migration (i), the migration of the requested service must be done before the device starts using it. With a proactive strategy, near-continuous service is provided; however, it requires a preliminary decision. In case of an incorrect decision, unnecessary migration can be costly and degrade QoS. With the reactive strategy (ii), unnecessary migration can be avoided; however, partial outage of the service is likely because the migration process is induced after the handover of the IoT devices. Whichever strategy is used, the network properties, the physical distance, and the size of the service influence the length of the process. We focused on proactive migration mechanisms because service interruption is unacceptable for today's real-time applications. As a result, the service is already ready by the time the user's device is received by the new node. In order to make such decisions, the strategy has to decide when and where the service should be migrated. Typically it is initialized when the response time or the latency increase due to the distance between the node and the IoT device. To decide which other node (i.e., where) the service should be migrated, the actual load, network delay, and physical position can be considered. In this paper, a Markov chain was used for proactive migration prediction, because it is a popular method for mobility estimation due to its efficiency and intuitiveness [19]. The Markov model looks at a set of data and attempts to establish rules between the different directions of movement. The next estimate is based only on the measurements that preceded it. Hence, the \(k\)-order Markov model examines the \(k-1\) of data preceding the estimate. The Markov chain defines a so-called Markov space, which can be written as a vector of finite size.