TY - GEN
T1 - ASALP
T2 - 21st IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2025
AU - Liu, Hui
AU - Xiang, Hui
AU - Wu, Yong
AU - Liu, Zeguang
AU - Du, Junzhao
N1 - Publisher Copyright:
© IFIP International Federation for Information Processing 2026.
PY - 2026
Y1 - 2026
N2 - 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.
AB - 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.
KW - Edge Autonomy
KW - Horizontal Auto Scaler
KW - KubeEdge
KW - Kubernetes
KW - Time Series Prediction
UR - https://www.scopus.com/pages/publications/105023274050
U2 - 10.1007/978-3-032-10466-3_32
DO - 10.1007/978-3-032-10466-3_32
M3 - Conference contribution
AN - SCOPUS:105023274050
SN - 9783032104656
T3 - Lecture Notes in Computer Science
SP - 386
EP - 398
BT - Network and Parallel Computing - 21st IFIP WG 10.3 International Conference, NPC 2025, Proceedings
A2 - Wang, Xiaoliang
A2 - Ye, Baoliu
A2 - Jiang, Xiaohong
A2 - Crespi, Noel
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 14 November 2025 through 16 November 2025
ER -