Poster: ISOML: Inter-Service Online Meta-Learning for Newly Emerging Network Traffic Prediction
Migyeong Kang, Juho Jung, Minhan Cho, Daejin Choi, Eunil Park, Sangheon Pack, Jinyoung Han · 2024
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.