# Resource Management in SDN-Based Cloud and SDN-Based Fog Computing: Taxonomy Study
## 5 SDN-Based Fog Computing
The evaluation is conducted via testbed using two datasets for training and testing. The results show that the maximum latency achieved equal to 12.5 milliseconds and saved energy up to 87%. To solve the problem of packet delays imposed by the SDN controller in fog networking, a prediction model is proposed based on regression model which follow standard process of data mining methodology [44]. The process generally consists of data understanding, which aims to build an initial dataset, and data preparation, which aims to clean dataset, remove redundancy and construct features, as well, modeling, which includes selection and assessments machine learning algorithms that results in selecting random forest algorithm. The results shows that proposed model manages to predict delay values similar to actual values. Moreover, a solution for SDN-fog services based on machine learning--specifically, reinforcement learning--is proposed in [45] in order to allocate service dynamically based on objectives such as power consumption, security and QoS constraints. Proposed reinforcement learning imposes recurrent neural networks in order to reduce the average response time. The service manager is built-in SDN controller specifies the locations of services to be processed dynamically by taking into consideration overall objective. In [46], the proposed work goal is to increase storage, by reducing the cache redundancy and improving service placement with limited interactions with controller, therefore merging software-defined networks and named data network in fog computing is introduced. By extracting powerful features of both paradigms i.e., devoting controllability to SDN controller to perform routing, function placement, caching decision and service allocation. While the data plane is managed by forwarding information base, content store and pending interest table, which are offered by NDN stateful data plane. The evaluation is conducted using MATLAB against SDN-IP protocol systems by comparing the number of packets exchanged between the nodes and controller and number of new rules inserted by the controller. The results show SDN-NDN systems are capable of reducing communication between nodes and the controller. In [47], data collection schema and estimation model for SDN-based vehicular fog computing is proposed. The architecture of SDN-based VFC consists of three layers, which are an infrastructure layer that includes vehicles, a fog layer that includes both regional controllers and local SDN controllers and a cloud layer, which includes a global SDN controller. The main tasks consist of the data collection configuration process, fog station deployments and data routing. A collection of data configuration consists of four main phases that are initialization of data collection parameters that are performed by a global SDN controller as data-oriented or fog-oriented. Then, the data collection parameter the sends collected data to fog stations. After that, users' requests are sent to fog stations. Finally, update data collection parameters to fit current environment, which include the deployment of fog station and data migration. A model is built to estimate number of fog stations required to be deployed and evaluation is conducted based on real traffic data. The results show that accurate estimation of fog stations is achieved, which depends on data availability and volume of data collection. While the data collection schema is evaluated in Veins Simulation Framework against classical schema to assess the latency, a number of hops to reach the fog station, recovery time and data loss. The results show that a proposed schema decreases the latency and number of hops. As well, less time is required for recovery, and less data loss is achieved.