🤖 AI Summary
This work addresses the significant underestimation of service-level energy consumption in cloud-native microservices, which commonly arises from focusing solely on computational overhead while neglecting network and storage components—leading to underestimations of up to 63%. To bridge this gap, we propose GOXN, the first service-level experimental engine capable of full-stack energy modeling encompassing computation, networking, and storage. Built on Kubernetes, GOXN integrates container-level metrics from Kepler and cAdvisor and incorporates OpenTelemetry Demo, service mesh, and distributed tracing systems. Experimental results demonstrate that under high tracing loads, the energy consumption of non-computational components becomes substantial, revealing that reliance on computational metrics alone severely underestimates the true energy cost of auxiliary services.
📝 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.