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 the NAPES traffic generator for efficient economical and technical deployment in IoT networking systems evaluation.
Highlights
This paper presents a solution developed in the FlexNet project related to the management of network resources using Software-Defined
Procedures to collect traffic from a network using ifstat; Procedures to compute the previously described observations from the traffic; Objective function fed to Ray that expresses the total throughput in a network; Dynamically modified network topologies that can change during training between episodes; Dynamically selected currently considered intents that change between episodes; Support for dynamic traffic that can vary between iterations
To test our Artificial Intelligence (AI) solution, we use Networked Application Emulation System (NAPES), a traffic generator we developed within the FlexNet project
Summary
This paper presents a solution developed in the FlexNet (www.celticnext.eu/projectflexnet) project related to the management of network resources using Software-Defined. The FlexNet project proposes flexible resource management using SDN with the support of an AI solution for different IoT use cases. 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 used in our ongoing FlexNet project [14,15,16]. New applications and end-user devices generate different network traffic patterns; 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
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