AI 中文总结
研究针对微服务能耗问题，提出Spanergy能源感知分布式追踪方法，将微服务功率测量与追踪关联，通过实验验证其可行性，该方法开销适度，能为节能微服务提供可操作见解。
AI 中文摘要
云计算因提供看似无限的虚拟资源而日益普及，但云数据中心的电力消耗不断增长。微服务是云应用的重要组成部分，需要新的解决方案来观察其能耗。分布式追踪广泛用于诊断基于微服务应用的延迟和故障，但未揭示单个终端用户请求的能源成本。本文提出Spanergy，一种能源感知分布式追踪方法，将每个微服务的功率测量与追踪相关联，并将测量的能耗归因于请求段。通过同步请求链和微服务间的异步交互展示了Spanergy。提出了严格的实验协议和统计分析计划来量化开销并验证守恒和覆盖属性。启用OpenTelemetry追踪相对于未检测的基线使总实验能量增加了59.1%，Spanergy后处理增加了基线能量的15.2%。结果表明能源感知追踪在适度开销下是可行的，为节能微服务提供了可操作的见解。
英文摘要
Cloud computing is gaining popularity by giving access to seemingly unlimited virtual resources. However, Cloud data centres are built with physical resources and their electricity consumption has been continuously growing over the past decades. Microservices are an important building block of Cloud applications, calling for new solutions to observe their energy consumption. Distributed tracing is widely deployed to diagnose latency and failures in microservice-based applications, yet it does not expose the energy cost of individual end-user requests. Such a gap limits energy-aware debugging, accountability, and control. This paper presents Spanergy, an energy-aware distributed tracing approach that correlates per-microservice power measurements with traces and that attributes measured energy consumption to request segments, i.e. trace spans. We showcase Spanergy with synchronous request chains and asynchronous interactions across microservices. We present a rigorous experimental protocol and statistical analysis plan to quantify overhead and to validate conservation and coverage properties on realistic configurations. Enabling OpenTelemetry tracing increased total experiment energy by 59.1% relative to the uninstrumented baseline, and Spanergy post-processing added 15.2% of the baseline energy. Hence, Spanergy's incremental energy cost is smaller than the energy overhead of enabling tracing itself, making the approach lightweight in practice. Spanergy also reveals that a non-negligible fraction of request energy comes from spans outside the latency-critical path. These results show that energy-aware tracing is feasible at modest overhead and provides actionable insights for energy-efficient microservices.
DOI：10.1109/CCGrid68966.2026.00036
