**2.1 Hierarchical control plane SDN architecture powered by AI**

In this subsection, we first introduce an SDN and a hierarchical architecture followed by an AI powered SDN architecture. The SDN consists of data, control, and application planes. **Figure 1** [26] shows the typical SDN architecture. Forwarding devices like routers and switches are part of the data plane. The centralized controller is part of the control plane. At the top is the application plane where different applications can be deployed and executed for a variety of purposes, such as routing, load balancing, security, and monitoring. The controller shall act as a strategic control point for the underlying network. However, several issues arise from a single controller in the SDN. For example, if the controller fails due to a software or hardware problem, the entire network that depends on the controller will collapse.

In addition, the controller will experience a performance bottleneck if the number of switches in its domain increases or the request messages towards it increases. Furthermore, traffic loads are not evenly distributed over the network. As a result,

### *Management of Software-Defined Networking Powered by Artificial Intelligence DOI: http://dx.doi.org/10.5772/intechopen.97197*

multiple controllers should be used for viewing the E2E network. However, if there are multiple heterogeneous domains, there is a need for consistency and collaboration between domains for the provisioning of E2E QoS.

**Figure 2** shows the hierarchical control plane SDN architecture. In the proposed architecture there are local controllers which has access of the data planes of the local domains. Global controllers (GCs) in the hierarchical control plane architecture have access to the global view of physically distributed local data plane switches. The hierarchical architecture of SDN controllers integrates autonomous domains with hierarchical associations. Multiple domains are integrated with the hierarchical architecture of the controller, where the local domain controllers (LCs) coordinate via the GC. By applying hierarchical architecture, new services can be easily managed and deployed in domains that coexist on the E2E path between the source and the destination [27] nodes.

The tasks handled by the controller are propagated from the lower LC layer to the upper GC layer, which reduces computational complexity. The hierarchical control plane with a global view reduces the E2E delay as the network scales [28]. In the proposed architecture, the GC acts proactively to set up the E2E path and therefore reduces the delay in setting up the path (the delay in setting up the path and pushing the flow entries into the switches) [29]. The hierarchical architecture enables communication between multiple LCs with a variety of equipment. The effectiveness of the hierarchical control plane for effective collaboration between heterogeneous tactical networks with a guaranteed QoS has been demonstrated in [30, 31]. The rewards for state action pairs in the Q-learning are therefore more accurate than the local view states because these rewards with a hierarchical architecture reflect the E2E view of the underlying network.

In our proposed SDNMS-PAI, a hierarchical control plane architecture is employed to construct a completely global view and control for geographical distributed network and build a global AI agent through the global control plane to generate a network control policy via reinforcement learning algorithms. The SDNMS-PAI can intelligently control and optimize a network to meet the differentiated network requirements in a large-scale dynamic network. In the following subsections, we describe the proposed AI enabled SDN architecture from bottom to top. The SDNMS-PAI is shown in **Figure 3**.
