GreenWise: Intelligent Application Migration for Containerized Machine Learning Services in the Computing Continuum

Abstract

Machine Learning (ML) services require both responsiveness and efficiency. In the dynamic Computing Continuum (CC), service migration has emerged as an important strategy to optimize service performance, improve power efficiency, or reduce operational costs. A basis enabler of the service migration in the CC is containerization and container orchestration, however, dynamically moving the service among Cloud and Edge servers based on several uncertainties is still challenging. In this paper, we present GreenWise, a framework for intelligent service migration for containerized ML services in the heterogeneous CC. GreenWise extends the monitoring agents to continuously monitor power consumption and performance metrics across diverse layers and sources in real-time, providing a holistic view of states. Leveraging Reinforcement Learning (RL), it enables Power-Performance aware strategies to achieve near-optimal online migration decisions under dynamic conditions. We perform GreenWise on top of a Kubernetes-based CC platform, implementing agents for intelligent migration for containerized ML services. Experimental results demonstrate that GreenWise achieves effective trade-offs between performance and power efficiency. Our proposed Power-Latency-Aware (MaskPPO) migration strategy outperforms Random and Round-Robin baselines 159.2% and 13.8% in a real cluster. These results highlight GreenWise’s potential for sustainable and intelligent ML service migration in the dynamic CC.