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Edge computing provides inherent advantages of low latency and user proximity; however, it encounters significant challenges in achieving resource elasticity and balancing dynamic traffic loads. The default scaling mechanism in Kubernetes, the Horizontal Pod Autoscaler (HPA), adopts a reactive strategy that restricts its capacity to address real-time demands and exhibits limited effectiveness in edge environments. To overcome these limitations, we introduce ASALP (Automatic Scaling Architecture for Edge Node Resources based on Load Prediction), which augments the Kubernetes–KubeEdge framework with an enhanced RWKV-EFE load prediction model and incorporates Nginx, Consul, and Prometheus to enable dynamic load balancing. Evaluated on the MQPS dataset, RWKV-EFE achieves substantially lower mean squared error (MSE) and mean absolute error (MAE), reducing them by 28.71% and 12.58% compared with FEDformer, and by 77.24% and 53.88% compared with Autoformer. Furthermore, in comparison with HPA, THPA, reactive ASALP, and ASALP-FEDformer, ASALP improves the request success rate by 57.17%, 21.33%, 14.62%, and 7.59%, respectively, while also alleviating the adverse effects of unstable communication links. These experimental results confirm the effectiveness of ASALP in enabling efficient resource scaling and load balancing for real-world edge computing deployments.

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ASALP: An Automatic Scaling Architecture for Edge Node Resources Based on Load Prediction

  • 会议论文

  • 发表日期：

    2026年

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  • Hui Liu,
  • Hui Xiang,
  • Yong Wu,
  • Zeguang Liu,
  • Junzhao Du

摘要

Edge computing provides inherent advantages of low latency and user proximity; however, it encounters significant challenges in achieving resource elasticity and balancing dynamic traffic loads. The default scaling mechanism in Kubernetes, the Horizontal Pod Autoscaler (HPA), adopts a reactive strategy that restricts its capacity to address real-time demands and exhibits limited effectiveness in edge environments. To overcome these limitations, we introduce ASALP (Automatic Scaling Architecture for Edge Node Resources based on Load Prediction), which augments the Kubernetes–KubeEdge framework with an enhanced RWKV-EFE load prediction model and incorporates Nginx, Consul, and Prometheus to enable dynamic load balancing. Evaluated on the MQPS dataset, RWKV-EFE achieves substantially lower mean squared error (MSE) and mean absolute error (MAE), reducing them by 28.71% and 12.58% compared with FEDformer, and by 77.24% and 53.88% compared with Autoformer. Furthermore, in comparison with HPA, THPA, reactive ASALP, and ASALP-FEDformer, ASALP improves the request success rate by 57.17%, 21.33%, 14.62%, and 7.59%, respectively, while also alleviating the adverse effects of unstable communication links. These experimental results confirm the effectiveness of ASALP in enabling efficient resource scaling and load balancing for real-world edge computing deployments.