Aceso：面向微服务的延迟感知地理分布，以实现高性价比的碳减排
Aceso: Latency-Aware Geo-Distribution of Microservices for Cost-Effective Carbon Reduction
Georgia Christofidi, Francisco Álvarez-Terribas, Ioannis Roumpos, Nicolas Kourtellis, Jesus Omaña Iglesias, Thaleia Dimitra Doudali
AI 速览：Aceso 是一种面向区域性受限基础设施的自适应微服务放置方法，在满足延迟约束的前提下，同时考虑碳强度与成本，动态调整微服务的地理分布。
Microservices are a dominant architecture in cloud computing, offering scalability and modularity, but also posing complex deployment challenges. As data centers contribute significantly to global carbon emissions, carbon-aware scheduling is a promising mitigation strategy. However, most existing solutions target batch, HPC, or serverless workloads and assume global-scale infrastructure. Such an assumption does not hold for many regional small to medium-sized enterprises (SMEs) with microservice applications, due to geographic, cost, and data sovereignty constraints. In this paper, we present Aceso, an Adaptive Carbon- and Efficiency-aware placement for microservices that considers carbon, cost, and latency constraints. Aceso dynamically places microservices across geographically constrained regions using a scalable optimization with insight-based search space pruning techniques. Evaluation on a real-world deployment shows that Aceso adapts to real-time changes in workload and carbon intensity and reduces carbon emissions by up to 37.4% and operational cost by up to 3.36%, on average across days, compared to a static deployment within a single country, while maintaining the application's latency targets. In this way, Aceso enables carbon- and cost-aware microservice deployment for latency-sensitive applications in regionally limited infrastructures.