# Artificial Intelligence Control Logic in Next-Generation Programmable Networks
###### Abstract  
The new generation of programmable networks allow mechanisms to be deployed for the efficient control of dynamic bandwidth allocation and ensure Quality of Service (QoS) in terms of Key Performance Indicators (KPIs) for delay or loss sensitive Internet of Things (IoT) services. To achieve flexible, dynamic and automated network resource management in Software-Defined Networking (SDN), Artificial Intelligence (AI) algorithms can provide an effective solution. In the paper, we propose the solution for network resources allocation, where the AI algorithm is responsible for controlling intent-based routing in SDN. The paper focuses on the problem of optimal switching of intents between two designated paths using the Deep-Q-Learning approach based on an artificial neural network. The proposed algorithm is the main novelty of this paper. The Developed Networked Application Emulation System (NAPES) allows the AI solution to be tested with different patterns to evaluate the performance of the proposed solution. The AI algorithm was trained to maximize the total throughput in the network and effective network utilization. The results presented confirm the validity of applied AI approach to the problem of improving network performance in next-generation networks and the usefulness of theNAPES traffic generator for efficient economical and technical deployment in IoT networking systems evaluation.  
artificial intelligence; deep-Q-learning; internet of things; software defined networking; programmable networks; IoT traffic generation Article  
Article Intelligence Control Logic in Next-Generation Programmable Networks  
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This new network paradigm is fully aligned with the efforts currently ongoing in the implementation of 5G technology, providing high-quality and consistent connectivity for people and objects, creating the perception of infinite capacity. The FlexNet project proposes flexible resource management using SDN with the support of an AI solution for different IoT use cases.  
Usually, the network is managed manually using commands, scripts or special tools, without automation for the efficient allocation of network resources. For several years, new network solutions with improved management solutions can be seen, where the most advanced one is the SDN solution [1; 2; 3]. In [4], the basic mechanisms and techniques of SDN for dynamic and scalable network control envisioned for 5G technology are defined [5]. One of the main elements is the SDN controller, which allows adaptive dynamic provisioning of resources by applying management rules to the traffic flow in the network [6]. On the other hand, the traffic generated in the network is also becoming more complex, especially in IoT applications [7; 8; 9; 10; 11] where the management of large data volumes requires more flexibility and scalability [12; 13].  
This flexibility and efficiency approach is often supported by artificial intelligence (AI) algorithms. In our proposed approach, the AI algorithm supports the intent routing control in SDN and is responsible for the resource allocation mechanism and network parameters such as throughput and network losses. This approach is also used in our ongoing FlexNet project [14; 15; 16]. The FlexNet project covers different use cases related to IoT technology, where different Quality of Service (QoS) requirements have to be met.  
In addition, an IoT traffic generator called Networked Application Emulation System (NAPES) was developed and used for solution validation purposes. New applications and end-user devices generate different network traffic patterns; therefore, for validating new network solutions in complex application scenarios, the aforementioned IoT traffic generator is a very useful, cost-effective and fast-to-implement tool.