Energy consumption is an increasing concern in IoT-Edge-Cloud infrastructures, where containerized application orchestration must balance performance with sustainability. This paper investigates how the Kubernetes CODECO framework integrates cross-layer energy-awareness into scheduling decisions for containerized applications across the IoT-Edge-Cloud continuum. CODECO monitors energy at both the computational level, via Kepler, and at a network (IP) level, and uses these metrics to define greenness heuristics that guide pod placement decisions through its ILP-based scheduler. The approach is experimentally evaluated on a real-world far Edge testbed composed of ARM-based embedded devices, comparing CODECO against vanilla Kubernetes across multiple scenarios. The results show that CODECO consistently reduces the energy consumption of the cluster, with savings of up to 11.01 mJ in computational energy and 4.14 mJ in network transmission energy consumption at peak load, for a wide set of scenarios which combine different types of injected fault conditions, including CPU stress, asymmetric network delay, and bandwidth contention. A composite greenness score combining both energy dimensions provides a stable and consistent ranking of scheduling strategies across all conditions, demonstrating its suitability as a unified energy indicator for cluster-level orchestration decisions across the IoT-Edge-Cloud continuum.

翻译：能耗是物联网-边缘-云基础设施中日益关切的问题，其中容器化应用编排必须平衡性能与可持续性。本文研究了Kubernetes CODECO框架如何将跨层能量感知整合到跨物联网-边缘-云连续体的容器化应用调度决策中。CODECO通过Kepler在计算层面以及网络（IP）层面监测能量，并利用这些指标定义绿色启发式规则，通过其基于ILP的调度器引导Pod放置决策。该方法在由基于ARM的嵌入式设备组成的真实远边缘测试床上进行了实验评估，在多种场景下将CODECO与原生Kubernetes进行了比较。结果表明，CODECO始终能降低集群能耗，在一系列结合不同类型注入故障条件（包括CPU压力、非对称网络延迟和带宽竞争）的场景中，峰值负载下计算能耗节省高达11.01毫焦，网络传输能耗节省4.14毫焦。结合两种能量维度的综合绿色评分在所有条件下提供了稳定且一致的调度策略排序，证明了其作为跨物联网-边缘-云连续体集群级编排决策的统一能量指标的适用性。