**4. Results and discussion**

Results of the proposed SDNMS-PAI are compared with existing ones i.e., software-defined networking with no artificial intelligence (SDN-NAI) [32]. There are 5 domains on the E2E path and two layers of the controllers i.e., local controllers and a global controller. We consider delay, jitter, and PLR as the primary QoS parameters in every domain. Controllers of the five domains are assigned to 50 nodes according to the controller placement in [43].

**Figure 4** compares the E2E delay (in milliseconds (ms)) from source to destination for the SDN-NAI i.e., SDN with no artificial intelligence enabled global control plane and our proposed SDNMS-PAI with. We can see that the delay for the initial service requests is greater for the SDNMS-PAI because the AI agent explores the E2E paths from source to destination for the optimal service classes. However, as the AI agent learns about the global optimum policy, then the delay decrease as compared to SDN-NAI which is shown in the 3rd, 4th, and 5th domains. Initially the service request rates are smaller hence the delay is low however with increasing the service request rate the delay increases because of the consumption of the available bandwidth resources on the E2E paths.

The results in **Figure 5** show that E2E jitter (ms) from source to destination for an SDN-NAI compared with SDNMS-PAI. The figure reveals that the jitter for the initial service requests is greater for the SDNMS-PAI due to the AI agent exploring the E2E paths from source to destination to find the optimal service classes. However, as the AI agent becomes more proficient in learning about the global optimum policy, then the jitter decreases as compared to SDN-NAI, which is shown in the 3rd, 4th, and 5th domains. Initially, with lower service request rate the jitter is low since each service request requires only a portion of the available bandwidth on an E2E path. With increasing the service request rate, however, the jitter will increase because of the bandwidth resources used in each service request.

**Figure 4.** *E2E delay from source to destination with increasing service requests passing through five domains.*

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

**Figure 5.** *E2E jitter from source to destination with increasing service requests passing through five domains.*

**Figure 6.** *Packet loss ratio (PLR) with increasing service request rate.*

**Figure 6** compares the PLR with increasing the service request rate. Herein, the PLR is the ratio of the number of received packets divided by the total number of packets against each service request from source to destination. We can see from **Figure 6** that the PLR is initially high for the SDNMS-PAI however as the AI agent obtains a global optimum then the PLR does not increase in the same rate with SDN-NAI. However, the overall PLR increase with increasing the service request rate because the available resources in the network gets occupied.

**Figure 7** shows a comparison of the E2E DC ratio for SDNMS-PAI and SDN-NAI. We can see from the figure that the SDN-NAI DC ratio was initially higher than the SDNMS-PAI. However, as the AI agent learns, the DC ratio for the proposed scheme is much higher than the SDN-NAI ratio. The basic reason is that, as the service

**Figure 7.** *E2E DC ratio against service requests.*

requests increase, the overall DC ratio becomes low due to the consumption of the available bandwidth on the E2E pathways. Nevertheless, the E2E DC ratio is still 1 or greater than 1 for the proposed SDNMS-PAI, which means that it satisfies the QoS requirements for the application service request. In addition, it overcomes the SDN-NAI in E2E DC ratio.
