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

Georgia Christofidi, Francisco Álvarez-Terribas, Ioannis Roumpos, Nicolas Kourtellis, Jesus Omaña Iglesias, Thaleia Dimitra Doudali

TL;DR

Aceso dynamically places microservices across geographically constrained regions using a scalable optimization strategy that leverages insight-based search space pruning techniques and enables carbon- and cost-aware microservice deployment for latency-sensitive applications in regionally limited infrastructures for SMEs.

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.

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

TL;DR

Aceso dynamically places microservices across geographically constrained regions using a scalable optimization strategy that leverages insight-based search space pruning techniques and enables carbon- and cost-aware microservice deployment for latency-sensitive applications in regionally limited infrastructures for SMEs.

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.
Paper Structure (23 sections, 1 equation, 11 figures, 2 tables)

This paper contains 23 sections, 1 equation, 11 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Microservice Applications
  4. Carbon Intensity
  5. Financial Feasibility
  6. Insights from a Real Deployment
  7. Aceso System Design
  8. Overview and Objectives
  9. Traffic Forecaster
  10. Workload Profiler
  11. Placement Optimizer
  12. Scheduler
  13. Implementation Details
  14. Evaluation
  15. Real-world Deployment
  16. ...and 8 more sections

Figures (11)

  • Figure 1: Hourly carbon intensity across European and North American cloud regions across one week of August.
  • Figure 2: Monthly cloud cost across global regions, colored by continent.
  • Figure 3: Overview of the microservice DAG and evaluated placement configurations in the real-world experiment.
  • Figure 4: Comparison of carbon, monthly cost, latency and SLO violations for the placement configurations of Table \ref{['tab:placements']}.
  • Figure 5: System Components of Aceso.
  • ...and 6 more figures