Green Autoscaler for Performance Aware Microservices: a Machine Learning Approach

Cloud-native microservice systems increasingly rely on autoscaling to maintain performance under fluctuating workloads, yet scaling decisions strongly influence energy consumption and carbon emissions, making sustainability a growing concern for modern cloud infrastructures. Traditional mechanisms such as the Kubernetes Horizontal Pod Autoscaler optimize only performance metrics and ignore the carbon intensity of electricity sources, leading to excessive provisioning and higher emissions. To address this limitation, this paper proposes a Carbon-Aware Autoscaling System based on Spatio-Temporal Graph Convolutional Networks that jointly model workload dynamics and inter-service dependencies while integrating real-time regional carbon intensity. The autoscaler dynamically moderates scaling levels using carbon-aware thresholds, enabling adaptive tradeoffs between performance and sustainability. We evaluated our approach on three benchmark microservice applications using a synchronized monitoring stack for performance, energy, and carbon metrics. Experimental results show that the proposed approach achieves an average carbon emission reduction of approximately 24% in high-intensity regions (300 gCO2/kWh) and 17% in low-intensity regions (100 gCO2/kWh) as compared to HPA while maintaining comparable performance in low-carbon conditions.