دورية أكاديمية
Swarm intelligence-based packet scheduling for future intelligent networks
| العنوان: | Swarm intelligence-based packet scheduling for future intelligent networks |
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| المؤلفون: | See, Chan H., Husen, Arif, Chaudary, Muhammad Hasanain, Ahmed, Farooq, Farooq-i-Azam, Muhammad, Ghani, Arfan |
| بيانات النشر: | PeerJ |
| سنة النشر: | 2023 |
| المجموعة: | Edinburgh Napier Repository (Napier University Edinburgh) |
| مصطلحات موضوعية: | TIPS, Machine learning, Data mining, Emerging technologies |
| الوصف: | 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. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 2376-5992 |
| العلاقة: | http://researchrepository.napier.ac.uk/Output/3212009Test; https://napier-repository.worktribe.com/file/3212009/1/Swarm%20intelligence-based%20packet%20scheduling%20for%20future%20intelligent%20networks%20%28accepted%20version%29Test |
| DOI: | 10.7717/peerj-cs.1671 |
| الإتاحة: | https://doi.org/10.7717/peerj-cs.1671Test https://napier-repository.worktribe.com/file/3212009/1/Swarm%20intelligence-based%20packet%20scheduling%20for%20future%20intelligent%20networks%20%28accepted%20version%29Test http://researchrepository.napier.ac.uk/Output/3212009Test |
| حقوق: | openAccess |
| رقم الانضمام: | edsbas.B52C054B |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 23765992 |
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| DOI: | 10.7717/peerj-cs.1671 |