Aceso: Carbon-Aware and Cost-Effective Microservice Placement for Small and Medium-sized Enterprises

2026-03-11

AI Summary

This work addresses the challenges faced by small and medium-sized enterprises in deploying microservices within regional infrastructure—namely high carbon emissions, elevated operational costs, and the absence of carbon-aware scheduling strategies. To this end, we propose Aceso, an adaptive microservice placement approach tailored for geographically constrained environments. Aceso is the first to jointly incorporate real-time carbon intensity and dynamic workload patterns into a unified optimization framework, simultaneously minimizing both carbon footprint and cost while adhering to latency constraints. Leveraging insight-driven search space pruning and a scalable optimization algorithm, Aceso efficiently resolves the placement problem. Empirical evaluations from real-world deployments demonstrate that our method reduces carbon emissions by 37.4% and operational costs by 3.6% on average, all while consistently meeting service-level objectives (SLOs).

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• microservice placement
• carbon-aware scheduling
• small and medium-sized enterprises
• cost-effectiveness
• geographically constrained infrastructure

Innovation

• carbon-aware scheduling
• microservice placement
• search space pruning
• cost-effective optimization
• regional cloud infrastructure