# A Comprehensive Survey on Knowledge-Defined Networking
## 4 Management Plane
### Architecture of the Management Plane  
Figure 5 shows the expanded architecture of the management plane with detailed flows between each functional block in the management plane and other planes [57, 58]. As shown in Figure 5, on the top of the architecture, there is an application plane for defining policies for making network control and management decisions, whereas, on the bottom, there is infrastructure (KDN or legacy switches). The Network Service Abstraction (NSA) layer is an interface for the control and management decision-making applications to connect with the rest of the architecture, which is an abstraction of the network services such as email services, file transfer, domain name systems, etc. [246]. The NSA layer provides a common interface for applications to communicate their service requirements to the knowledge, control, and management planes. The management plane (management abstraction layer) acts as an interface between the network services abstraction layer and the physical infrastructure of the network. Note that, in Figure 5, the control module works in parallel with the management abstraction layer. Figure 5: Architecture of the KDN management plane with detailed flows exchanged between management plane and other planes. The management abstraction layer provides a unified interface for data collection, device configuration, and monitoring of the underlying network, as shown in Figure 5. Raw data can be collected from the network and can be either directly used for network monitoring or provided to the knowledge plane for knowledge generation. The management information base is responsible for collecting and managing network data required for network monitoring tasks and knowledge generation. Note that raw data can also be collected by the control plane for either information-centric decision-making or to provide to the knowledge plane and use the generated knowledge or rules for decision-making. Data collected by each of the management and control planes have been discussed in Sections 4.5.2 and 5.4.1, respectively. Network device monitoring can be either on-demand or continuous in nature. The output of a network monitoring task (monitoring output) can be provided to the knowledge plane for further knowledge/rule generation, as shown in Figure 5. The network monitoring module will generate configuration rules by comparing data or knowledge available with network monitoring policies [247]. These configuration rules will be provided to the network configuration module to directly configure the data plane elements, as shown in Figure 5. For example, consider a network energy-monitoring task. Assume that there is a network monitoring policy that specifies that \"The average energy consumption of the network should be below 50%\". Assume that the network monitoring module detects that \"more than 50% of network nodes have an energy consumption greater than 90%\", either using an information-centric approach or a knowledge-centric approach. Now, as the network monitoring output clearly indicates that the network monitoring policy is violated, it can create and issue a configuration rule to the network configuration module, such as \"Reduce energy consumption of each node by 20%\". Note that, in this example, monitoring output is \"more than 50% of nodes have more than 90% energy consumption\", which can be provided to the knowledge plane to further generate knowledge and use that knowledge to dynamically update energy management policies in the application plane. Note that, if network monitoring output is provided to the knowledge plane, further knowledge or rules on the monitoring output can be generated, and that output can have an effect on the application layer's policies. The management plane should frequently query the switches to collect the data it requires, such as flow statistics, network topology, configuration, etc. Monitoring accuracy and network overhead depend on the frequency of polling, where a higher frequency yields both high accuracy and high overhead.