@inproceedings{2b36effb2d7c49708c68a86cd45a1c41,
title = "Leveraging Artificial Neural Networks for Accurate Short-Term Traffic Prediction in Centralized SDN Architecture",
abstract = "This work is built on the congestion avoidance model in software-defined networks by employing an efficient algorithm to forecast load and modify methods used in data communication. Because of its versatility, cutting-edge technology like artificial intelligence may be included, improving network performance and offering superior solutions to big issues. In particular, the congestion avoidance problem and the QoS are the advanced approaches that form the basis of the combined algorithm that is proposed for this research in order to achieve more efficient results. The simulations accurately assess the performance of the suggested algorithm, and the results are found in the various evaluation parameters. Because the algorithm is crucial to the network architecture because of the software-defined network's (SDN) adaptability and flexibility, the other outcomes are derived from the simulation's diversity of parameters.",
keywords = "SDN, machine learning, network performance, quality of service, traffic load prediction",
author = "Behdad Bibak and Michel Kadoch and Qia Li and Tao Hong",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.; 3rd International Conference on Information Processing and Network Provisioning, ICIPNP 2024 ; Conference date: 14-06-2024 Through 16-06-2024",
year = "2026",
doi = "10.1007/978-981-96-6465-8\_31",
language = "英语",
isbn = "9789819664641",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "356--370",
editor = "Michel Kadoch and Mohamed Cheriet and Xuesong Qiu",
booktitle = "Information Processing and Network Provisioning - 3rd International Conference, ICIPNP 2024, Proceedings",
address = "德国",
}