Modern cloud platforms are facing a sharp increase in power demand driven by the rapid adoption of AI-powered applications, making power optimization urgent under net-zero commitments and sustainability goals. Yet, reducing power in production remains challenging for latency-sensitive microservices, where performance violations directly affect user experience and operational risk. Such services exhibit heterogeneous workload characteristics and dynamic load patterns. In multi-tenant environments, contention on shared uncore resources, including last-level cache and memory bandwidth, can degrade performance, especially for memory-intensive workloads. As a safeguard, providers often run servers in performance mode, fixing core and uncore frequencies at high levels. Existing power governors largely ignore application-level performance requirements and uncore interference, leading to systematic power over-provisioning. To address this, we introduce K8SPI, a hierarchical reinforcement learning controller that jointly optimizes CPU core and uncore frequencies for cloud-native deployments. K8SPI uses a two-stage architecture: a coarse-grained agent rapidly mitigates performance violations, while a fine-grained agent minimizes power once requirements are satisfied. Using telemetry from hardware, Kubernetes, and application layers, K8SPI adapts to workload heterogeneity and cross-microservice interference. We evaluate K8SPI on a Kubernetes testbed across multiple scenarios. Results show that K8SPI reduces node-level power by 23--30\% compared with the Linux performance governor while keeping performance requirement violations below 2--3\%, even under severe uncore contention and dynamic load fluctuations.

翻译：[摘要] 现代云平台正面临由AI驱动应用快速普及所引发的电力需求急剧增长，这使得在净零承诺与可持续发展目标下，功耗优化变得刻不容缓。然而，对于延迟敏感的微服务而言，在保证性能不违规（直接关乎用户体验与运营风险）的前提下降低生产环境功耗仍具挑战性。此类服务呈现出异构的工作负载特征与动态的负载模式。在多租户环境中，共享非核心资源（包括末级缓存与内存带宽）上的争用会降低性能，尤其对于内存密集型负载。作为安全措施，提供商通常将服务器运行于性能模式，将核心与非核心频率固定在高水平。现有功耗调控器大多忽略应用级性能需求与非核心干扰，导致系统性的功耗过度配置。为此，我们提出K8SPI——一种分层强化学习控制器，用于联合优化云原生部署中CPU核心与非核心频率。K8SPI采用两阶段架构：粗粒度代理快速缓解性能违规，而细粒度代理在满足需求后最小化功耗。通过利用来自硬件层、Kubernetes层与应用层的遥测数据，K8SPI自适应于工作负载异构性及跨微服务干扰。我们在Kubernetes测试平台上对K8SPI进行了多场景评估。结果表明，与Linux性能调控器相比，K8SPI将节点级功耗降低了23%–30%，同时即使在严重的非核心争用与动态负载波动下，也能将性能需求违规率控制在2%–3%以下。