Design and Implementation of a High-availability Network Infrastructure for Large-scale Interactive E-learning

Large-scale interactive online education platforms present significant challenges to traditional elastic scaling strategies based on static thresholds due to their dynamic and unpredictable load characteristics. This article designs and implements a cloud native high-availability network infrastructure centered around an intelligent elastic scaling model that integrates time series prediction and reinforcement learning. This architecture deeply integrates microservices and service mesh technology, predicting short-term resource requirements through historical load and contextual information (such as course schedules), and driving Kubernetes clusters to perform pre-scaling. The research is validated through simulation analysis and real prototype system experiments. The results show that in the simulation environment, the model improves resource prediction accuracy by 25% compared to traditional Horizontal Pod Autoscaler (HPA) strategies, and reduces service level agreement (SLA) violation rates by more than 60% during sudden traffic. In practical systems, the average response delay during peak periods is reduced by 40%, resource utilization increases by 35%, and system availability reaches 99.99%, significantly improving service quality and resource utilization efficiency.