AI 中文总结
针对云与边缘计算能源需求增长及碳排放问题，提出EcoKube框架，用于在异构边缘云环境中评估可持续性感知调度策略，含事件驱动模拟器等，通过合成批处理工作负载评估，可在部署前比较策略。
AI 中文摘要
云与边缘计算的能源需求迅速增长，人工智能工作负载加剧了电力使用和碳排放。在混合边缘云环境中，可持续性影响取决于随时间和地点变化的电网碳强度（CI）、站点能源使用效率（PUE）和异构硬件特性。现有碳感知工作探索了如时间弹性、时空工作负载转移等解决方案，但缺乏评估异构联合边缘云拓扑上可持续性感知调度策略的一致且可重现的工作流程。我们提出EcoKube，一个用于在异构边缘云环境中可重现地评估可持续性感知调度策略的可配置模拟框架，包括事件驱动的确定性模拟器、策略钩子和异构感知参考策略。我们用合成批处理工作负载评估该框架，将参考策略与默认的Kubernetes调度器、KEIDS和TOPSIS/KCSS进行比较。其贡献在于架构和实验方面：EcoKube提供了在部署前比较可持续性感知策略的可重现方法。
英文摘要
Energy demand from cloud and edge computing is rising rapidly, with AI workloads further intensifying electricity use and associated carbon emissions. In hybrid edge-cloud settings, sustainability impact depends on time- and location-varying grid Carbon Intensity (CI), site Power Usage Effectiveness (PUE), and heterogeneous hardware characteristics. Existing carbon-aware work explores solutions such as temporal elasticity, spatio-temporal workload shifting, and carbon-aware placement across distributed sites. However, these solutions do not provide a consistent and reproducible workflow for evaluating sustainability-aware scheduling policies on heterogeneous, federated edge-cloud topologies. We present EcoKube: a configurable simulation framework for the reproducible evaluation of sustainability-aware scheduling policies in heterogeneous edge-cloud environments. The framework includes an event-driven deterministic simulator, policy hooks, and a heterogeneity-aware reference policy. We evaluate the framework with synthetic batch workloads, comparing the reference policy against the default Kubernetes scheduler, KEIDS, and TOPSIS/KCSS. The contribution is architectural and experimental: EcoKube provides a reproducible way to compare sustainability-aware policies before deployment.
Comments6 pages, 2 figures. Submitted to EuroSys2026 (TDIS 2026) workshop
Journal refProceedings of the 4th International Workshop on Testing Distributed Internet of Things Systems (TDIS 2026), ACM, 2026, pp. 7-12
