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

TL;DR

This paper introduces GOXN, a tool for precise service-level energy measurement in cloud-native microservices, revealing significant underestimations when excluding network and storage energy components.

Contribution

We present GOXN, a novel energy experimentation engine that accurately measures compute, network, and storage energy at the service level in Kubernetes environments.

Findings

Excluding network and storage can underestimate auxiliary-service energy by up to 63%.

High tracing loads increase the energy contribution of network and storage.

Service-level energy modeling improves accuracy over container-level estimates.

Abstract

Recent advancements enable fine-grained energy measurements in cloud-native environments (e.g., at container or process level) beyond traditional coarse-grained scopes. However, service-level energy measurement for microservice-based applications remains underexplored. Such measurements must include compute, network, and storage energy to avoid underestimating consumption in distributed setups. We present GOXN (Green Observability eXperiment eNginE), an energy experimentation engine for Kubernetes-based microservices that quantifies compute, network, and storage energy at the service level. Using GOXN, we evaluated the OpenTelemetry Demo under varying configurations (monitoring, tracing, service mesh) and steady synthetic load, collecting metrics from Kepler and cAdvisor. Our additive energy model derives service-level energy from container-level data. Results show that excluding network and storage can underestimate auxiliary-service energy by up to 63%, and that high tracing loads shift energy dominance toward network and storage.