Merge pull request #110 from rootfs/power-doc

document power model specs

docs/design/power_model.md

@@ -9,6 +9,8 @@ In Kepler, with respective to available measurements, we provide a pod-level pow
 
     also see [Get started with Kepler Model Server](../kepler_model_server/get_started.md)
 
+- **Pre-trained Power Models**: We provide pre-trained power models for different deployment scenarios. Current x86_64 pretrained model are developed in [Intel® Xeon® Processor E5-2667 v3](https://github.com/sustainable-computing-io/kepler-model-db/tree/main/models). Models with other architectures are coming soon. You can find these models in [Kepler Model DB](https://github.com/sustainable-computing-io/kepler-model-db/tree/main/models/v0.6/nx12). These models support both power ratio modeling and power estimation modeling for both RAPL and ACPI power sources. The `AbsPower` models estimate both idle and dynamic power while the `DynPower` models only estimate dynamic power. The MAE (mean absolute error) of these models are also published. Kepler container image has preloaded [acpi/AbsPower/BPFOnly/SGDRegressorTrainer_1.json](https://github.com/sustainable-computing-io/kepler-model-db/blob/main/models/v0.6/nx12/std_v0.6/acpi/AbsPower/BPFOnly/SGDRegressorTrainer_1.json) model for node energy estimate and [rapl/AbsPower/BPFOnly/SGDRegressorTrainer_1.json](https://github.com/sustainable-computing-io/kepler-model-db/blob/main/models/v0.6/nx12/std_v0.6/rapl/AbsPower/BPFOnly/SGDRegressorTrainer_1.json) for Container absolute power estimate. 
+
 ## Usage Scenario
 
 Scenario | Node Total Power | Node Component Powers | Pod Power

docs/design/power_model.zh.md

@@ -7,6 +7,8 @@
 
 -**功率估计建模**：该建模通过使用度量作为训练模型的输入特征来估计功率。即使不能测量功率度量，也可以使用此建模。估计可以分为三个级别：节点总功率（包括风扇、电源等）、节点内部组件功率（如CPU、内存）、Pod功率。另请参阅[开始使用Kepler模型服务器](../kepler_model_server/get_started.md)
 
+-**预训练功率模型**：我们为不同的部署场景提供预训练的功率模型。当前的x86_64预训练模型是在[Intel® Xeon® Processor E5-2667 v3](https://github.com/sustainable-computing-io/kepler-model-db/tree/main/models)中开发的。其他架构的模型即将推出。您可以在[Kepler Model DB](https://github.com/sustainable-computing-io/kepler-model-db/tree/main/models/v0.6/nx12)中找到这些模型。这些模型支持RAPL和ACPI电源的功率比例建模和功率估算建模。AbsPower模型估算静态和动态功率，而DynPower模型只估算动态功率。这些模型的MAE（平均绝对误差）也已发布。Kepler容器镜像已预加载[acpi/AbsPower/BPFOnly/SGDRegressorTrainer_1.json](https://github.com/sustainable-computing-io/kepler-model-db/blob/main/models/v0.6/nx12/std_v0.6/acpi/AbsPower/BPFOnly/SGDRegressorTrainer_1.json)模型用于节点能量估算，以及[rapl/AbsPower/BPFOnly/SGDRegressorTrainer_1.json](https://github.com/sustainable-computing-io/kepler-model-db/blob/main/models/v0.6/nx12/std_v0.6/rapl/AbsPower/BPFOnly/SGDRegressorTrainer_1.json)用于容器绝对功率估算。
+
 ## 使用场景
 
 Scenario | Node Total Power | Node Component Powers | Pod Power