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Aceso is introduced as an adaptive microservice placement strategy tailored for SMEs with geographically constrained infrastructure, optimizing for carbon emissions, cost, and latency. It uses insight-based search space pruning to dynamically place microservices based on real-time workload and carbon intensity changes. Experiments on a real-world deployment demonstrate that Aceso achieves a 37.4% reduction in carbon emissions and a 3.6% cost reduction compared to static deployments, while meeting service level objectives (SLOs).
SMEs can slash carbon emissions by 37% and costs by 3.6% simply by using Aceso's carbon-aware microservice placement, even with regionally limited infrastructure.
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.
