# Modeling Dew Computing in DISSECT-CF-Fog
## 3 The Proposed Model for Dew Computing
### Limited Internet Access  
The two main features of Dew computing are (i) _independence_ when the functionality of the cloud service is still available even with a limited internet connection, and (ii) _collaboration_, when the information sampled and collected is exchanged automatically with the cloud service. It can happen continuously or when internet access recovers. Dew computing extends the cloud functionalities to the end users' gadgets, such as laptops, smartphones, and so on, which is often called a Dew server.  
The different positions of the local Dew servers are depicted in Figure 2. Since distance can have a critical impact on service response times, in this work, the movements of the devices are also considered (_device_, _device_, _device_). The increasing latency of the computing nodes (_node_, _node_) is depicted by the ranges (_range_(_node_), _range_(_node_)), the closer a device is to the boundary of the range, the higher the latency it deals with. These limitations are considered as follows: _device_ is located inside two ranges; therefore, it can decide which node will be advantageously utilized by the device. Various properties can be involved in such decisions (utilization cost, latency, workload). Moreover, _device_ is limited to communicating with the center node of the range. Finally, _device_ is too far from any center nodes, which means it is considered a Dew server in a non-internet zone.  
However, if it is reached in the future by its movement (any range covered by at least one node), the device would be able to begin communicating with a more beneficial fog node. Typically, the nodes with fewer computing tasks and shorter response times are preferred by the devices. On the contrary, if any device leaves a range (i.e., the response time/latency becomes intolerable), it will act as a Dew server.  
Figure 2: Limited internet access to smart devices.  
For Dew computing, only smart devices are considered that have sufficient networking, storing, and processing abilities; thus, the service can be migrated to the associated smart device. However, this should be handled with care, as it can greatly affect the battery life of the device or slow down other tasks. In most cases, the capacity of the end users' devices is more limited than fog and cloud nodes, so computational tasks may take more time to complete.  
Nevertheless, today's smart devices, such as laptops, tablets, personal computers, and phones, fulfil the requirements of Dew computing to serve as Dew servers.  
To model a smart/Dew device as similar as possible to a fog/cloud node, virtual layers should be provided. As most Dew devices have general purpose memories and CPUs, this is an easy task. In the virtualized Dew device architecture, we assume that there is only one VM at a time, which is vertically scalable, (i.e., more resources can be allocated to a given VM if needed). On the other hand, a standard fog server is also horizontally scalable (i.e., it can run multiple VMs at the same time).  
As a result, the device behaving as a Dew server (running a Dew service) will be different form a full-fledged fog node, as it will not be able to serve other devices, but will only respond to data from its own domain, which is processed and evaluated locally.