**1. Introduction**

Due to the rapid development of Internet technology, network terminals have been widely spread. However, traditional network architectures have failed to adapt to future advances in communication and Internet technologies, resulting in heterogeneous networks. As a result, the existing network infrastructure was unable to keep up with the rapid changes of the Internet. A key feature of traditional network architectures is that the data and control planes are tightly coupled, which has some limitations. For example, if you want to change the network configuration, you need to configure each device independently across the entire network which is a daunting task.

Similarly, vendors are reluctant to provide the internal details of the device to developers and users, as changes in the configuration of existing networking devices can lead to malfunctions in the network. In addition, the protocol is strongly built into the firmware of network devices. These limitations hinder network innovation due to proprietary hardware and lack of testing for innovative networking solutions due to their distributed nature. It also increases the management workload and the overall cost of network management.

On the other hand, Software Defined Networking (SDN) [1–5] has revolutionized network management by separating data and control planes. The data plane is composed of forwarding devices, for example routers, switches, etc. Its main functions are forwarding the packets according to the policies of the controller. If the destination of the arrived packets is not found in the forwarding devices, then those packets are sent to the controller by the data plane. The control plane, however, is implemented through intelligent SDN controllers such as OpenDaylight (ODL), Open Networking Operating System (ONOS), POX and RYU [6]. Control plane obtains the status of the underlying network and defines the policies for the packets arriving on the forwarding devices. It then pushes the updated rules to the data plane. The separation of data and control planes has shifted network complexity from networking devices to smart SDN controllers. Thus, the network can be programmed through the application running on the controller and the underlying network is abstracted from the applications [7]. The innovative concept presented by SDN has the great advantage of flexible and efficient network configuration, network management and operation. Therefore, SDN is expected to be an excellent choice for the next generation of telecommunication networks and Internet technologies. Because of these benefits, large information technology organizations such as Facebook, Amazon, and Google have implemented SDN to connect remote data centers [8, 9].

The internet has grown in recent years. As a result, there is a huge increase in the amount of network traffic. Because the accuracy of machine learning algorithms depends mainly on the availability of historical data. There is therefore an increasing tendency towards the use of machine learning techniques. Because the accuracy of machine learning algorithms increases with sufficient data. For this reason, researchers now prefer to apply machine learning solutions because, once trained on the available data, the trained model generates accurate results on the new data through learning experience. The introduction of 5G heterogeneous networks and the rapid ubiquitous use and growth of Internet data processing requirements are rapidly increasing as a result of a dramatic increase in the number of connected devices. For example, the heterogeneous IoT devices in 5G runs different protocols and various technologies results in increasing the traffic load [10]. In addition, there is a need for self-organization and demand-based networks to deal with huge amounts of data. SDN was therefore at the heart of the growing needs of such applications due to their programming, orchestration, and automation characteristics [11].

The SDN has been successfully deployed in data centers and enterprise traffic engineering networks across remote data centers. However, the adoption of SDN in the modern and global Internet still presents a number of challenges that need further investigation. As the internet is scaling and the traffic on the underlying network is dynamically changing. The application of an optimum policy for the underlying network should therefore be adapted in line with the radical changes in the internet. One of the problems in SDN is the configuration of the control plane, because the manual configuration is a costly task, because the traditional SDN approach [12–15] is not optimal in selecting the optimum policy for the underlying network. In addition, repeatedly reconfiguring the policy according to changes in the network will require the control plane to be reconfigured. One of the main issues, therefore, is the automatic orchestration of the control plane [16]. Because the rigid configuration of the control plane will have problems in the optimal configuration of the policy.

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

Another issue is the end-to-end (E2E) quality-of-service (QoS) performance of heterogeneous network providers. If the same provider manages SDN controllers, user applications and forwarding devices on the enterprise network, then, the network status of the underlying devices is readily available for upper-layer applications. However, the Internet consists of different providers where end-users, applications and service providers are often heterogeneous. As a result, the status of the network is not directly available for applications running on the upper layers.

Several solutions have been proposed to address the issue of the allocation of E2E resources [17–23]. However, they depend on the traditional and manual configuration of the control plane. i.e., once a policy has been defined for the underlying network. The behavior of the network is then controlled accordingly, regardless of the scale of the network or the dynamic changes. The policy of controlling the network is therefore not always optimal. Moreover, these solutions do not provide effective management of the SDN due to scaling up, increasing network complexity and dynamic changes. There is therefore a need to find a global optimal solution with an excellent value for the objective functions. We therefore propose a software-defined networking management system powered by AI (SDNMS-PAI) architecture to auto-configure policy management and E2E resource allocation.

The advantage of AI based architecture is that the AI agent will interact with the underlying network through the SDN controller for pushing the global optimal policy flow rules in the forwarding devices. The controller will share the network status information with the AI agent and based on real time status of the network the AI agent will find the most appropriate actions to be taken. The actions will be pushed as the flow rules in the forwarding devices. AI can be used to bring a closedloop control of the SDN. The closed-loop control incorporates collection of data, analytics, and subsequent actions that are all based on the results of the analytics [24]. All components of the closed loop can be improved and enhanced by means of AI to improve the speed, accuracy and, ultimately, the effectiveness of the closed loop control.

The main contributions of this chapter are summarized as follows:

