# Artificial Intelligence Control Logic in Next-Generation Programmable Networks
## 5 Conclusions  
In this paper, the management of SDN network resources with AI support for IoT applications was presented. Particularly, the studied problem focuses on an optimal switching of intents between two available paths in an SDN networks using AI algorithm. In the developed architecture, the switching is performed based on Deep-Q-Network AI mechanism where its main purpose is to maximize the total throughput in the SDN network with minimal average total data loss rate for served IoT applications. The presented approach is an intelligent SDN management system with AI support, especially applicable in programmable next-generation networks (5G and beyond). Moreover, the network resource allocation in context of QoS assurance is a growing problem, especially for IoT services. The designed solution is suitable for fast and optimal resource allocation, especially in cases of emergency and delay-sensitive IoT services. To test the proposed AI solution, we used an IoT traffic generator called NAPES, developed within the FlexNet project. We compared the performance and quality parameters, i.e., total throughput in the network and average data loss rate, to evaluate the AI usefulness in the designed solution. The presented results confirm effectiveness of proposed AI approach, which significantly improves the overall Quality of Service and network performance. In particular, it maximizes the total network throughput as well as significantly reduces the average total data loss rate. The obtained results confirm the rightness of applying AI to the presented problem. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{5}{c}{**(a) Loss rate without AI**} \\ \hline
**Flow No. ** & \multicolumn{5}{c}{**Test Case**} & \multicolumn{5}{c}{**Average**} \\  & **1** & **2** & **3** & **4** & **5** & \\ \hline
1 & 38.10 & 0.57 & 36.41 & 16.40 & 9.46 & 20.19 \\
2 & 0.49 & 14.09 & 4.57 & 22.11 & 35.65 & 15.38 \\
3 & 9.85 & 37.60 & 28.85 & 20.44 & 31.19 & 25.59 \\
4 & 30.58 & 25.00 & 8.36 & 18.45 & 2.40 & 16.96 \\
5 & 26.18 & 26.62 & 4.40 & 2.65 & 35.22 & 19.01 \\
6 & 25.32 & 1.55 & 26.48 & 22.14 & 6.26 & 16.35 \\
7 & 3.26 & 14.36 & 20.22 & 17.21 & 6.81 & 12.37 \\
8 & 21.81 & 33.83 & 25.16 & 34.54 & 28.09 & 28.69 \\
9 & 9.39 & 4.76 & 3.94 & 6.23 & 27.89 & 10.44 \\
10 & 33.93 & 17.70 & 28.00 & 34.34 & 22.76 & 27.35 \\
11 & 1.81 & 17.67 & 13.38 & 19.84 & 22.88 & 15.12 \\
12 & 31.19 & 36.31 & 31.03 & 16.16 & 2.75 & 23.49 \\ \hline \hline \multicolumn{5}{c}{**(b) Loss rate with AI**} \\ \hline
**Flow No. ** & \multicolumn{5}{c}{**Test Case**} & \multicolumn{5}{c}{**Average**} \\  & **1** & **2** & **3** & **4** & **5** & \\ \hline
1 & 1.49 & 5.98 & 4.08 & 3.33 & 3.57 & 3.69 \\
2 & 2.29 & 0.24 & 11.14 & 0.15 & 2.07 & 3.18 \\
3 & 0.58 & 7.79 & 19.35 & 1.23 & 12.34 & 8.26 \\
4 & 5.63 & 1.18 & 0.38 & 3.40 & 0.65 & 2.25 \\
5 & 4.47 & 19.34 & 10.17 & 13.74 & 3.52 & 10.25 \\
6 & 10.13 & 1.07 & 6.17 & 13.91 & 20.23 & 10.30 \\
7 & 7.12 & 3.26 & 1.55 & 4.56 & 13.07 & 5.91 \\
8 & 1.73 & 3.22 & 8.11 & 0.66 & 4.04 & 3.55 \\
9 & 3.71 & 1.35 & 8.34 & 11.21 & 9.27 & 6.77 \\
10 & 8.03 & 13.9 Further research will be focused on more comprehensive experiments that include real scenarios from IoT use cases with more extensive network topologies, with different QoS parameters (losses and latency) and with real traffic scenarios from IoT vertical services, loss and delay sensitive. Conceptualization, M.Z. and S.K. ; methodology, M.Z., S.K., A.B.. J.D. and Z.K. ; software, M.Z., J.D., A.B. and W.S. ; validation, S.K., Z.K. and A.B. ; formal analysis, M.Z. and Z.K. ; data curation, A.B., S.K. and Z.K. ; writing--original draft preparation, S.K., M.Z., J.D., Z.K. and A.B. ; writing--review and editing, S.K., M.Z., J.D. and Z.K. ; supervision, Z.K. and S.K. ; project administration, Z.K. and S.K. ; funding acquisition, Z.K. All authors have read and agreed to the published version of the manuscript.