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

2025-03-11

AI Summary

Cloud-native applications operate in multi-tenant, shared cloud environments, where conventional energy-efficiency evaluation methods—relying solely on isolated local metrics (e.g., CPU utilization)—fail to capture holistic system-level energy behavior. To address this, we propose the first automated, scalable energy-efficiency experimentation framework tailored for Kubernetes-native applications, enabling joint quantification of energy consumption and QoS metrics (latency, throughput, error rate) across container, platform, and infrastructure layers. The framework tightly integrates eBPF for fine-grained observability, Prometheus for metric collection, and hardware power interfaces (RAPL/ACPI) for accurate energy measurement, and establishes an end-to-end sustainability assessment pipeline. Evaluation across multiple open-source cloud-native applications demonstrates up to 42% energy-efficiency variation among architectural variants, while precisely characterizing their trade-offs with P99 latency and service availability.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Search and Optimization: Evaluation and Analysis
• Application Domains: Software Engineering

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements
• Responsible Web: Sustainability and climate impact of web technologies

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.

Problem

• Measure energy efficiency in cloud-native applications
• Optimize energy use in multi-tenant cloud environments
• Evaluate trade-offs between energy efficiency and service quality

Innovation

• Automated framework for cloud-native energy efficiency
• Integrates service quality and sustainability metrics
• Compatible with Kubernetes-based applications