Artifact for Service-Level Energy Modeling and Experimentation for Cloud-Native Microservices
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908783426404352 |
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| author | Legler, Julian |
| author_facet | Legler, Julian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_16635 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Artifact for Service-Level Energy Modeling and Experimentation for Cloud-Native Microservices Legler, Julian Distributed, Parallel, and Cluster Computing 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. |
| title | Artifact for Service-Level Energy Modeling and Experimentation for Cloud-Native Microservices |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2601.16635 |