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.

翻译：近期进展使得云原生环境中的细粒度能耗测量（例如在容器或进程级别）超越了传统的粗粒度范围。然而，针对基于微服务的应用程序的服务级能耗测量仍未得到充分探索。此类测量必须涵盖计算、网络和存储能耗，以避免低估分布式设置中的能耗。我们提出了GOXN（绿色可观测性实验引擎），这是一个面向基于Kubernetes的微服务的能耗实验引擎，可在服务级别量化计算、网络和存储能耗。利用GOXN，我们在不同配置（监控、追踪、服务网格）和稳定合成负载下评估了OpenTelemetry演示应用，并从Kepler和cAdvisor收集了指标。我们的累加式能耗模型从容器的数据推导出服务级能耗。结果表明，排除网络和存储能耗可能导致辅助服务的能耗被低估高达63%，并且高追踪负载会使能耗主导因素转向网络和存储。