GreenPipe: Power Modeling for Containerized DNN Inference on Kubernetes Edge Nodes

2026-09-04

AI Summary

为解决ARM节点上无硬件功耗计数器的准确在线功耗估计问题，提出GreenPipe方法，通过外部功率计测量构建多资源回归模型。

Abstract

Distributed DNN inference is increasingly deployed in containerized edge-cloud environments, where workloads run on-device or are exposed to remote clients over the network. Accurate online power estimation on resource-constrained ARM nodes without hardware power counters such as RAPL remains a challenge, and CPU-only models fail to capture multi-resource behavior. We present GreenPipe, an automated profiling-training-validation pipeline that builds multi-resource regression models from external power meter measurements and attributes power to containers proportionally. GreenPipe is evaluated on a Raspberry Pi 4 edge node in a K3s edge-cloud testbed, covering DNN inference with three vision models, multiple precisions, thread counts, and both local and serving scenarios. System-level MAPE is 6.3-9.4%, improving over CPU-stress and utilization-only baselines by 26.9% MAPE on average. We jointly report inference latency and energy per inference, exposing performance-energy trade-offs across workload configurations.

Problem

• Power Estimation
• DNN Inference
• Containerized
• Edge Nodes
• Multi-resource

Innovation

• multi-resource regression models
• external power meter measurements
• containerized DNN inference
• resource-constrained ARM nodes
• automated profiling-training-validation pipeline