# A Comprehensive Survey on Knowledge-Defined Networking
## 4 Management Plane
### Network Data Collection  
#### 4.5.1 Data Collection Methods (DCMs)  
Note that the management plane can collect data for network monitoring (flow statistics, network topology, performance metrics, etc.) and network configuration (configuration data) using a management protocol such as SNMP/OF-CONFIG or using a network monitoring framework. One of the main purposes of the management plane is to collect network data appropriately in order to generate knowledge using the knowledge plane. Furthermore, in the KDN paradigm, both network monitoring and network configuration functions are knowledge-driven rather than information-driven, even though an information-driven approach can be utilized, even in KDN. Thus, in a knowledge-driven approach, raw data also contain management-related data such as topology data, configuration data, performance metrics data, traffic flow data, etc., which are provided to the knowledge plane for knowledge/rule generation to drive network monitoring and configuration functions. As there are data types that cannot be extracted using one of the network management protocols discussed in Section 4.3 or the network monitoring framework discussed in Section 4.4, such as environmental data, detailed network traffic data, policy data, etc., which are used for knowledge generation or network monitoring, it is necessary to investigate Data Collection Methods (DCMs) to collect such data for the management plane. In other words, standard management protocols or network monitoring frameworks have not been designed to collect the customized data required for knowledge generation.  
Data collection can occur in two modes. The first mode is the reactive mode, in which data from the infrastructure plane are pulled from the management plane reactively. The other mode is the proactive mode, where the nodes proactively send data to the higher planes without an explicit request from such planes. Usually, data are first collected by the nodes (devices), and then collected data are sent to the management plane by each node proactively [259]. However, this approach, which is usually used in the centralized architecture of KDN, is not the optimal way to collect data, as redundant data may be collected at the management plane, which yields higher communication costs, channel utilization, and latency. Therefore, a data collection optimization framework using Integer Quadratic Programming (IQP) has been proposed that minimizes total communication delay, communication cost, and communication overhead so that only a selected number of agents will unicast the collected data to the management plane while other nodes act as only broadcasting nodes [260]. Other techniques involve packet sampling techniques,which have been proposed to sample wildcard flow entries to be collected as data [261]. An adaptive network data collection system that dynamically selects network data collection nodes based on network status and also samples network traffic based on flow characteristics to reduce the volume of data collection has been utilized in [262]. A data collection methodology to collect sensor measurements in an energy-efficient manner in a wireless sensor network environment of a KDN has been studied in [263]. Another data collection approach in vehicular network environments proposes to use a predictive data collection algorithm that cooperatively uses the cellular and ad hoc interfaces, using ad hoc interfaces where possible to limit the use of the cellular interface and reduce the communication cost at which routing decisions for such multi-hop data collection have been realized based on the network status [264].