Abstract
The increasing utilization of newly emerging networks (e.g., private-5G) across industries underscores the need for accurate traffic prediction to manage network resources effectively. However, rapidly emerging networks face challenges in accurate prediction due to limited training data at the early stage and fluctuation in traffic load at the maintenance stage. In response, we propose ISOML (Inter-Service Online Meta-Learning), a novel traffic prediction pipeline designed for newly emerging networks. ISOML utilizes meta-learning to address data scarcity and employs the EWC (Elastic Weight Consolidation) for online learning to learn dynamics of traffic patterns. Experimental validation in real-world datasets demonstrates the efficacy of ISOML in predicting traffic for emerging network environments.
| Original language | English |
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| Title of host publication | MOBISYS 2024 - Proceedings of the 2024 22nd Annual International Conference on Mobile Systems, Applications and Services |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 718-719 |
| Number of pages | 2 |
| ISBN (Electronic) | 9798400705816 |
| DOIs | |
| State | Published - 4 Jun 2024 |
| Event | 22nd Annual International Conference on Mobile Systems, Applications and Services, MOBISYS 2024 - Minato-ku, Japan Duration: 3 Jun 2024 → 7 Jun 2024 |
Publication series
| Name | MOBISYS 2024 - Proceedings of the 2024 22nd Annual International Conference on Mobile Systems, Applications and Services |
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Conference
| Conference | 22nd Annual International Conference on Mobile Systems, Applications and Services, MOBISYS 2024 |
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| Country/Territory | Japan |
| City | Minato-ku |
| Period | 3/06/24 → 7/06/24 |
Bibliographical note
Publisher Copyright:© 2024 Copyright held by the owner/author(s).
Keywords
- meta learning
- online learning
- private networks
- private-5G
- traffic prediction