Swarm intelligence-based packet scheduling for future intelligent networks

Husen, Arif; Chaudary, Muhammad Hasanain; Ahmed, Farooq; Farooq-i-Azam, Muhammad; See, Chan H.; Ghani, Arfan

Abstract

Network operations involve several decision-making tasks. Some of these tasks are related to operators, such as extending the footprint or upgrading the network capacityties. Other decision tasks are related to network functionsnalities, such as traffic classifications, scheduling, capacity, coverage trade-offs, and policy enforcement. These decisions are often decentralized, and each network node makes its own decisions based on the preconfigured rules or policies. To ensure effectiveness, it is important essential that planning and functional decisions are in harmony.; however, human intervention-based decisions are subject to high costs, delays, and mistakes. On the other hand, mMachine learning has been used in different fields of life to automate decision processes intelligently.
Similarly, future intelligent networks are also expected to see intense use of machine learning and artificial intelligence techniques for functional and operational automation. This article investigates the current state-of-the-art methods for packet scheduling and related decision processes. Furthermore, it proposes a machine learning-based approach for packet scheduling for agile and cost-effective networks to address various issues and challenges. The analysis of the experimental results shows that the proposed deep learning-based approach can successfully address the challenges without compromising the network performance. For example, it has been seen that with mean absolute error MAE from 6.38 to 8.41 using with the proposed deep learning model, the packet scheduling can maintain 99.95% throughput, 99.97 % delay, and 99.94% jitter, which are much better as compared to the statically configured traffic profiles.

Citation

Husen, A., Chaudary, M. H., Ahmed, F., Farooq-i-Azam, M., See, C. H., & Ghani, A. (2023). Swarm intelligence-based packet scheduling for future intelligent networks. PeerJ Computer Science, 9, Article e1671. https://doi.org/10.7717/peerj-cs.1671

• Journal Article Type: Article
• Acceptance Date: Oct 8, 2023
• Online Publication Date: Nov 16, 2023
• Publication Date: 2023
• Deposit Date: Oct 9, 2023
• Publicly Available Date: Nov 16, 2023
• Print ISSN: 2376-5992
• Electronic ISSN: 2376-5992
• Publisher: PeerJ
• Peer Reviewed: Peer Reviewed
• Volume: 9
• Article Number: e1671
• DOI: https://doi.org/10.7717/peerj-cs.1671
• Keywords: TIPS, Machine learning, Data mining, Emerging technologies
• Public URL: http://researchrepository.napier.ac.uk/Output/3212009