为资源受限的无线网络控制器设计了分簇预测模型，提升高负载场景下的预测精度。
Cluster-Specific Predictive Modeling: A Scalable Solution for Resource-Constrained Wi-Fi Controllers
- 按特征聚类时序数据，为不同簇定制专用预测模型。
- 在高活跃度集群中，分簇模型的平均绝对误差更低。
- 适合大规模集中式无线网络的可扩展部署与资源优化。
本文系统分析了在中央控制器内存与计算资源受限的大规模管理型Wi-Fi网络中，通过集成聚类算法与模型评估技术实现预测建模优化的问题。基于特征的聚类方法结合主成分分析（PCA）与高级特征工程，将时序数据按共性分组，进而构建分簇专用预测模型。对比全局模型（GMs）与分簇模型的性能发现，在高活跃度集群中，分簇模型在平均绝对误差（MAE）指标上持续表现更优。研究分析了模型复杂度（及精度）与资源利用率之间的权衡，凸显了定制化建模方法的可扩展性。结果支持通过选择性部署模型实现自适应网络管理，优化资源分配，提升预测精度，并保障大规模集中式Wi-Fi环境下的可扩展运行。
原文摘要 · Abstract (English)
This manuscript presents a comprehensive analysis of predictive modeling optimization in managed Wi-Fi networks through the integration of clustering algorithms and model evaluation techniques. The study addresses the challenges of deploying forecasting algorithms in large-scale environments managed by a central controller constrained by memory and computational resources. Feature-based clustering, supported by Principal Component Analysis (PCA) and advanced feature engineering, is employed to group time series data based on shared characteristics, enabling the development of cluster-specific predictive models. Comparative evaluations between global models (GMs) and cluster-specific models demonstrate that cluster-specific models consistently achieve superior accuracy in terms of Mean Absolute Error (MAE) values in high-activity clusters. The trade-offs between model complexity (and accuracy) and resource utilization are analyzed, highlighting the scalability of tailored modeling approaches. The findings advocate for adaptive network management strategies that optimize resource allocation through selective model deployment, enhance predictive accuracy, and ensure scalable operations in large-scale, centrally managed Wi-Fi environments.
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