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.

In the contemporary landscape of software development, the transition towards microservices architecture is often a critical step for organizations aiming to enhance scalability and maintainability. However, maintaining quality standards during this transition is of paramount importance to ensure the continued success of the software system. In this paper, we introduce a migration to microservices approach that strictly considers quality constraints as the main driver of the migration. The approach is built on top of the knowledge acquired in our previous work where we used antipatterns analysis for the refactoring of microservices derived from the monolith. We applied our quality-driven migration approach to the industrial case study of BIM Italia. To measure the effectiveness of our approach we performed a comparative analysis of the time, cost, and effort-related dimensions between the quality-driven migration and the refactoring processes presented and applied in the same company in our previous work. Our findings highlight the effectiveness of our quality-driven migration process in reducing time, costs, and effort, showcasing its merits in practice. This research emphasizes the importance of quality-driven migration strategies when transitioning from monolithic systems to microservices. By employing these principles, organizations can migrate to microservices not only realizing the expected benefits of microservices adoption but also upholding essential quality standards.