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A Comprehensive Experimentation Framework for Energy-Efficient Design of Cloud-Native Applications

Sebastian Werner, Maria C. Borges, Karl Wolf, Stefan Tai

TL;DR

The paper addresses the challenge of measuring energy efficiency in cloud-native applications across multiple cloud layers, where traditional metrics like CPU utilization are insufficient. It introduces CLUE, an automated, extensible experimentation framework that combines a cross-layer metric suite with an architecture-centric experiment design to compare design alternatives. The authors demonstrate CLUE on the TeaStore benchmark, evaluating five variants (e.g., Monolith, Serverless, Service Reduction, Runtime Improvement) to reveal how tactics impact energy, cost, latency, and reliability, with results showing both expected and surprising effects depending on workload. CLUE enables early, actionable feedback for developers and supports integration into CI/CD pipelines, though the study notes measurement limitations and calls for broader tooling and architecture coverage to generalize the findings across cloud environments. The work advances practical energy-aware software design for cloud-native systems by providing a unified framework to assess trade-offs across layers and deployment paradigms.

Abstract

Current approaches to designing energy-efficient applications typically rely on measuring individual components using readily available local metrics, like CPU utilization. However, these metrics fall short when applied to cloud-native applications, which operate within the multi-tenant, shared environments of distributed cloud providers. Assessing and optimizing the energy efficiency of cloud-native applications requires consideration of the complex, layered nature of modern cloud stacks. To address this need, we present a comprehensive, automated, and extensible experimentation framework that enables developers to measure energy efficiency across all relevant layers of a cloud-based application and evaluate associated quality trade-offs. Our framework integrates a suite of service quality and sustainability metrics, providing compatibility with any Kubernetes-based application. We demonstrate the feasibility and effectiveness of this approach through initial experimental results, comparing architectural design alternatives for a widely used open-source cloud-native application.

A Comprehensive Experimentation Framework for Energy-Efficient Design of Cloud-Native Applications

TL;DR

The paper addresses the challenge of measuring energy efficiency in cloud-native applications across multiple cloud layers, where traditional metrics like CPU utilization are insufficient. It introduces CLUE, an automated, extensible experimentation framework that combines a cross-layer metric suite with an architecture-centric experiment design to compare design alternatives. The authors demonstrate CLUE on the TeaStore benchmark, evaluating five variants (e.g., Monolith, Serverless, Service Reduction, Runtime Improvement) to reveal how tactics impact energy, cost, latency, and reliability, with results showing both expected and surprising effects depending on workload. CLUE enables early, actionable feedback for developers and supports integration into CI/CD pipelines, though the study notes measurement limitations and calls for broader tooling and architecture coverage to generalize the findings across cloud environments. The work advances practical energy-aware software design for cloud-native systems by providing a unified framework to assess trade-offs across layers and deployment paradigms.

Abstract

Current approaches to designing energy-efficient applications typically rely on measuring individual components using readily available local metrics, like CPU utilization. However, these metrics fall short when applied to cloud-native applications, which operate within the multi-tenant, shared environments of distributed cloud providers. Assessing and optimizing the energy efficiency of cloud-native applications requires consideration of the complex, layered nature of modern cloud stacks. To address this need, we present a comprehensive, automated, and extensible experimentation framework that enables developers to measure energy efficiency across all relevant layers of a cloud-based application and evaluate associated quality trade-offs. Our framework integrates a suite of service quality and sustainability metrics, providing compatibility with any Kubernetes-based application. We demonstrate the feasibility and effectiveness of this approach through initial experimental results, comparing architectural design alternatives for a widely used open-source cloud-native application.

Paper Structure

This paper contains 22 sections, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Cloud-native Applications
  4. Architecting for Energy Efficiency
  5. Measuring and Managing Energy Efficiency
  6. Methodology
  7. Metrics
  8. Design
  9. CLUE
  10. Components
  11. Usage
  12. Showcase
  13. Evaluation Protocol
  14. Evaluation Results
  15. Monolith (MO)
  16. ...and 7 more sections

Figures (5)

  • Figure 1: The multi-layered nature of modern cloud-native software systems
  • Figure 2: The high level architecture of CLUE, showing the three main components and sub-components.
  • Figure 3: Steps and operations that happen in CLUE during an experiment.
  • Figure 4: Resource utilization and platform overhead for the five variants. For utilization, we compare actual consumption vs. provisioned resources. For overhead, we calculate the load not caused by SUT.
  • Figure 5: Consumption per requests for different workloads and wasted energy due to under-utilized scaling (average of all workloads)