*2.1.2 SB-API*

The Southbound Application Programming Interface (SB-API) provides an interface for data interaction with the local control plane. There are several protocols available for the interaction of the two planes, but the most popular is OpenFlow. OpenFlow provides a secure interface for communication between the controller and the switch. The status of the network topology and the policies for action from the global control plane are communicated to the data plane via the SB-API in the SDNMS-PAI. The White Paper [34] describes the advantages and flexibility of OpenFlow for the programming of forwarding devices. The concept of OpenFlow originated from Stanford University, and the OpenFlow Networking Foundation (ONF) consortium now performs the standardization tasks of OpenFlow.

### *2.1.3 Local control plane*

The data plane switches of each domain are connected to the LCs on the E2E path. The LCs interact with the data plane through SB-APIs. The AIGCP dynamically obtains the underlying network status from the LCs; therefore, it has access to the global topology. As a result, the AIGCP will provide resources from local controllers upon the arrival of a service request. LCs work together through GC, and service level agreements (SLAs) are exchanged through it. Each LC is equipped with a traffic flow template (TFT) module [35] containing the source and destination port numbers, the Internet Protocol (IP) addresses and the QoS parameters. The data collected will be used by the AIGCP for the allocation of E2E resources.

#### *2.1.4 NB-API*

The northbound application programming interface (NB-API) functions as a communication interface between the local control and AIGCP. The local control plane functions as a bridge between the forwarding devices and AIGCP utilizing the representational state transfer (REST) API. Similarly, the operational statistics (e.g., about the flow entries) from the data plane are available via this API to the global control plane AI agent. Reinforcement learning algorithms running in the global control plane communicates with the local control plane through this API and the corresponding actions are delegated to the data plane. These actions represent the behavior of the reinforcement learning algorithms executed in the global control plane. For example, a firewall application implements policies for

controlling the ingress and egress packets passing through the network. Therefore, the data plane devices will forward or block the traffic according to the rules defined in the application. Similarly, a load balancing algorithm will control the traffic through monitoring congestion in different paths of the network. Herein, we employ the Q-learning for E2E QoS provisioning.
