Service-Level Energy Modeling and Experimentation for Cloud-Native Microservices

2025-10-15

AI Summary

Microservice architectures enhance flexibility and scalability of cloud-native systems but introduce energy estimation inaccuracies due to cross-container service interactions—particularly with network- and storage-intensive auxiliary services. Existing container-level or system-level energy models fail to capture the distributed execution’s energy implications across service boundaries. Method: We propose the first service-level, fine-grained energy modeling approach that explicitly incorporates network and storage energy consumption into microservice interaction efficiency evaluation. Our method bridges the semantic gap between container-level and system-level models. Contribution/Results: Leveraging a configurable experimental platform, we conduct multi-scenario empirical studies on observability and monitoring auxiliary services. Results show that neglecting network and storage energy leads to up to 63% underestimation of auxiliary service energy consumption, demonstrating the critical value of service-level modeling for accurate microservice energy efficiency assessment.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Security and Privacy: Large-scale security measurements

Abstract

Microservice architectures have become the dominant paradigm for cloud-native systems, offering flexibility and scalability. However, this shift has also led to increased demand for cloud resources, contributing to higher energy consumption and carbon emissions. While existing research has focused on measuring fine-grained energy usage of CPU and memory at the container level, or on system-wide assessments, these approaches often overlook the energy impact of cross-container service interactions, especially those involving network and storage for auxiliary services such as observability and system monitoring. To address this gap, we introduce a service-level energy model that captures the distributed nature of microservice execution across containers. Our model is supported by an experimentation tool that accounts for energy consumption not just in CPU and memory, but also in network and storage components. We validate our approach through extensive experimentation with diverse experiment configurations of auxiliary services for a popular open-source cloud-native microservice application. Results show that omitting network and storage can lead to an underestimation of auxiliary service energy use by up to 63%, highlighting the need for more comprehensive energy assessments in the design of energy-efficient microservice architectures.

Problem

• Modeling energy consumption in cloud-native microservices across distributed containers
• Addressing overlooked energy impacts from network and storage interactions
• Quantifying underestimation of auxiliary service energy usage in microservices

Innovation

• Service-level energy model for distributed microservice execution
• Experimentation tool measuring CPU memory network storage energy
• Validated approach revealing 63% energy underestimation without network storage