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 has emerged as a promising mitigation strategy. However, most existing solutions target batch, high-performance, or serverless workloads and assume access to global-scale infrastructure. Such an assumption does not hold for many national or regional small to medium-sized enterprises (SMEs) with microservice applications, which represent the real-world majority. 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 strategy that leverages insight-based search space pruning techniques. Evaluation on a real-world deployment shows that Aceso quickly adapts to real-time changes in workload and carbon intensity and reduces carbon emissions by 37.4% and operational cost by 3.6%, on average, compared to a static deployment within a single country, while consistently meeting SLOs. In this way, Aceso enables carbon- and cost-aware microservice deployment for latency-sensitive applications in regionally limited infrastructures for SMEs.

翻译：微服务作为云计算领域的主流架构，在提供可扩展性与模块化优势的同时，也带来了复杂的部署挑战。数据中心在全球碳排放中占据显著比重，碳感知调度已成为一种具有前景的减排策略。然而，现有解决方案大多针对批处理、高性能或无服务器负载，且假设具备全球级基础设施的访问能力。对于现实中占多数的、部署微服务应用的国家或地区性中小企业而言，这一假设并不成立。本文提出Aceso——一种自适应碳感知与能效感知的微服务部署方案，该方案综合考虑碳排放、成本与延迟约束。Aceso通过基于智能洞察的搜索空间剪枝技术，采用可扩展的优化策略，在受地理限制的区域间动态部署微服务。真实场景部署评估表明：相较于单国静态部署方案，Aceso能快速适应工作负载与碳强度的实时变化，在持续满足服务等级协议的同时，平均降低37.4%的碳排放与3.6%的运营成本。由此，Aceso为中小企业在区域受限的基础设施中部署延迟敏感型应用，提供了兼具碳感知与成本效益的微服务部署方案。