From 08394cb9ac9dd08877dea4c8becb13f125765ae8 Mon Sep 17 00:00:00 2001 From: qmhu Date: Fri, 17 Jun 2022 18:22:13 +0800 Subject: [PATCH] update docs for smart recommend --- docs/index.md | 4 +- docs/index.zh.md | 8 +- .../tutorials/analytics-and-recommendation.md | 4 +- .../analytics-and-recommendation.zh.md | 10 +- docs/tutorials/replicas-recommendation.md | 36 +++- docs/tutorials/replicas-recommendation.zh.md | 181 +++++++++++------- docs/tutorials/resource-recommendation.md | 38 +++- docs/tutorials/resource-recommendation.zh.md | 19 +- .../analytics-workload-replicas.yaml | 15 ++ mkdocs.yml | 7 +- 10 files changed, 224 insertions(+), 98 deletions(-) create mode 100644 examples/analytics/analytics-workload-replicas.yaml diff --git a/docs/index.md b/docs/index.md index 5d7197e22..ef863c5ff 100644 --- a/docs/index.md +++ b/docs/index.md @@ -35,8 +35,8 @@ Analytics model analyzes the workload and provide recommendations about resource Two Recommendations are currently supported: -- [**ResourceRecommend**](tutorials/resource-recommendation.md): Recommend container requests & limit resources based on historic metrics. -- [**Effective HPARecommend**](tutorials/replicas-recommendation.md): Recommend which workloads are suitable for autoscaling and provide optimized configurations such as minReplicas, maxReplicas. +- [**ResourceRecommend**](tutorials/resource-recommendation.md): Replicas recommendation analyze the actual application usage and give advice for replicas and HPA configurations. +- [**HPARecommend**](tutorials/replicas-recommendation.md): Resource recommendation allows you to obtain recommended values for resources in a cluster and use them to improve the resource utilization of the cluster. Please see [this document](tutorials/analytics-and-recommendation.md) to learn more. diff --git a/docs/index.zh.md b/docs/index.zh.md index 143754a81..5f2f845b9 100644 --- a/docs/index.zh.md +++ b/docs/index.zh.md @@ -30,12 +30,10 @@ Please see [this document](tutorials/using-effective-hpa-to-scaling-with-effecti ### Analytics -Analytics model analyzes the workload and provide recommendations about resource optimize. +智能推荐能够帮助用户自动分析集群并给出优化建议。就像手机助手一样，智能推荐会定期的扫描、分析你的集群并给出推荐建议。目前，我们提供了两种优化能力： -Two Recommendations are currently supported: - -- [**ResourceRecommend**](tutorials/resource-recommendation.md): Recommend container requests & limit resources based on historic metrics. -- [**Effective HPARecommend**](tutorials/replicas-recommendation.md): Recommend which workloads are suitable for autoscaling and provide optimized configurations such as minReplicas, maxReplicas. +- [**资源推荐**](tutorials/resource-recommendation.zh.md): 通过资源推荐的算法分析应用的真实用量推荐更合适的资源配置，您可以参考并采纳它提升集群的资源利用率。 +- [**副本数推荐**](tutorials/replicas-recommendation.zh.md): 通过副本数推荐的算法分析应用的真实用量推荐更合适的副本和 EHPA 配置，您可以参考并采纳它提升集群的资源利用率。 Please see [this document](tutorials/analytics-and-recommendation.md) to learn more. diff --git a/docs/tutorials/analytics-and-recommendation.md b/docs/tutorials/analytics-and-recommendation.md index 1d4e5a3e6..c19fc49ee 100644 --- a/docs/tutorials/analytics-and-recommendation.md +++ b/docs/tutorials/analytics-and-recommendation.md @@ -4,8 +4,8 @@ Analytics and Recommendation provide capacity that analyzes the workload in k8s Two Recommendations are currently supported: -- [**ResourceRecommend**](resource-recommendation.md): Recommend container requests & limit resources based on historic metrics. -- [**HPARecommend**](replicas-recommendation.md): Recommend which workloads are suitable for autoscaling and provide optimized configurations such as minReplicas, maxReplicas. +- [**ResourceRecommend**](resource-recommendation.md): Replicas recommendation analyze the actual application usage and give advice for replicas and HPA configurations. +- [**HPARecommend**](replicas-recommendation.md): Resource recommendation allows you to obtain recommended values for resources in a cluster and use them to improve the resource utilization of the cluster. ## Architecture diff --git a/docs/tutorials/analytics-and-recommendation.zh.md b/docs/tutorials/analytics-and-recommendation.zh.md index 72db54a81..804fb2548 100644 --- a/docs/tutorials/analytics-and-recommendation.zh.md +++ b/docs/tutorials/analytics-and-recommendation.zh.md @@ -1,9 +1,11 @@ -# 分析和推荐 +# 智能推荐 -分析和推荐提供了一套自动化的成本优化能力，它帮助用户自动的发现问题并提供优化的方案。就像电脑/手机助手一样，它会定期的扫描、分析你的集群并给出推荐建议。目前，我们提供了两种优化能力： +智能推荐能够帮助用户自动分析集群并给出优化建议。就像手机助手一样，智能推荐会定期的扫描、分析你的集群并给出推荐建议。目前，我们提供了两种优化能力： -- [**资源推荐**](resource-recommendation.zh.md): Kubernetes 的用户在创建应用资源时常常是基于经验值来设置 request 和 limit。通过资源推荐的算法分析应用的真实用量推荐更合适的资源配置，您可以参考并采纳它提升集群的资源利用率。 -- [**弹性推荐**](replicas-recommendation.zh.md): 筛选工作负载，推荐出适合做弹性的工作负载并给出弹性建议 +- [**资源推荐**](resource-recommendation.zh.md): 通过资源推荐的算法分析应用的真实用量推荐更合适的资源配置，您可以参考并采纳它提升集群的资源利用率。 +- [**副本数推荐**](replicas-recommendation.zh.md): 通过副本数推荐的算法分析应用的真实用量推荐更合适的副本和 EHPA 配置，您可以参考并采纳它提升集群的资源利用率。 + +应用可以根据资源推荐调整 request 也可以根据副本数推荐调整副本数，这两种优化都能帮助您降低成本，您可以根据您的需求选择采用相应的优化建议。 ## 架构 diff --git a/docs/tutorials/replicas-recommendation.md b/docs/tutorials/replicas-recommendation.md index d33d74c64..d01510f3e 100644 --- a/docs/tutorials/replicas-recommendation.md +++ b/docs/tutorials/replicas-recommendation.md @@ -1,6 +1,12 @@ # Replicas Recommendation -Using hpa recommendations, you can find resources in the cluster that are suitable for autoscaling, and use Crane's recommended result to create an autoscaling object: [Effective HorizontalPodAutoscaler](using-effective-hpa-to-scaling-with-effectiveness.md). +Kubernetes' users often set the replicas of workload or HPA configurations based on empirical values. Replicas recommendation analyze the actual application usage and give advice for replicas and HPA configurations. You can refer to and adopt it for your workloads to improve cluster resource utilization. + +## Features + +1. Algorithm: The algorithm for calculating the replicas refers to HPA, and supports to customization algo args +2. HPA recommendations: Scan for applications that suitable for configuring horizontal elasticity (EHPA), And give advice for configuration of EHPA, [EHPA](using-effective-hpa-to-scaling-with-effectiveness.md) is a smart horizontal elastic product provided by Crane +3. Support batch analysis: With the ResourceSelector, users can batch analyze multiple workloads ## Create HPA Analytics @@ -50,7 +56,7 @@ kubectl get analytics nginx-replicas -o yaml The output is similar to: -```yaml hl_lines="32" +```yaml apiVersion: analysis.crane.io/v1alpha1 kind: Analytics metadata: @@ -164,6 +170,32 @@ metadata: selfLink: "" ``` +## Batch recommendation + +Use a sample to show how to recommend all Deployments and StatefulSets by one `Analytics`: + +```yaml +apiVersion: analysis.crane.io/v1alpha1 +kind: Analytics +metadata: + name: workload-replicas + namespace: crane-system # The Analytics in Crane-system will select all resource across all namespaces. +spec: + type: Replicas # This can only be "Resource" or "Replicas". + completionStrategy: + completionStrategyType: Periodical # This can only be "Once" or "Periodical". + periodSeconds: 86400 # analytics selected resources every 1 day + resourceSelectors: # defines all the resources to be select with + - kind: Deployment + apiVersion: apps/v1 + - kind: StatefulSet + apiVersion: apps/v1 +``` + +1. when using `crane-system` as your namespace，`Analytics` selected all namespaces，when namespace not equal `crane-system`，`Analytics` selected the resource that in `Analytics` namespace +2. resourceSelectors defines the resource to analysis，kind and apiVersion is mandatory，name is optional +3. resourceSelectors supoort any resource that are [Scale Subresource](https://kubernetes.io/docs/tasks/extend-kubernetes/custom-resources/custom-resource-definitions/#scale-subresource) + ## HPA Recommendation Algorithm model ### Inspecting diff --git a/docs/tutorials/replicas-recommendation.zh.md b/docs/tutorials/replicas-recommendation.zh.md index b44be5811..5cc92a53a 100644 --- a/docs/tutorials/replicas-recommendation.zh.md +++ b/docs/tutorials/replicas-recommendation.zh.md @@ -1,6 +1,12 @@ -# 弹性推荐 +# 副本数推荐 -通过弹性推荐，你可以发现集群中适合弹性的资源，并使用 Crane 推荐的弹性配置创建自动弹性器: [Effective HorizontalPodAutoscaler](using-effective-hpa-to-scaling-with-effectiveness.md) +Kubernetes 用户在创建应用资源时常常是基于经验值来设置副本数或者 EHPA 配置。通过副本数推荐的算法分析应用的真实用量推荐更合适的副本配置，您可以参考并采纳它提升集群的资源利用率。 + +## 产品功能 + +1. 算法：计算副本数的算法参考了 HPA 的计算公式，并且支持自定义算法的关键配置 +2. HPA 推荐：副本数推荐会扫描出适合配置水平弹性（EHPA）的应用，并给出 EHPA 的配置, [EHPA](using-effective-hpa-to-scaling-with-effectiveness.md) 是 Crane 提供了智能水平弹性产品 +3. 支持批量分析：通过 `Analytics` 的 ResourceSelector，用户可以批量分析多个工作负载 ## 创建弹性分析 @@ -27,9 +33,9 @@ apiVersion: analysis.crane.io/v1alpha1 kind: Analytics metadata: - name: nginx-hpa + name: nginx-replicas spec: - type: HPA # This can only be "Resource" or "HPA". + type: Replicas # This can only be "Resource" or "Replicas". completionStrategy: completionStrategyType: Periodical # This can only be "Once" or "Periodical". periodSeconds: 600 # analytics selected resources every 10 minutes @@ -38,66 +44,65 @@ spec: apiVersion: apps/v1 name: nginx-deployment config: # defines all the configuration for this analytics - ehpa.deployment-min-replicas: "1" - ehpa.fluctuation-threshold: "0" - ehpa.min-cpu-usage-threshold: "0" + replicas.workload-min-replicas: "1" + replicas.fluctuation-threshold: "0" + replicas.min-cpu-usage-threshold: "0" ``` 结果如下: ```bash -NAME AGE -nginx-hpa 16m +NAME AGE +nginx-replicas 16m ``` -查看 Analytics 的 Status，通过 status.recommendations[0].name 得到 Recommendation 的 name: +查看 Analytics 详情: ```bash -kubectl get analytics nginx-hpa -o yaml +kubectl get analytics nginx-replicas -o yaml ``` 结果如下: -```yaml hl_lines="32" +```yaml apiVersion: analysis.crane.io/v1alpha1 kind: Analytics metadata: - creationTimestamp: "2022-05-15T13:34:19Z" - name: nginx-hpa + name: nginx-replicas namespace: default spec: completionStrategy: completionStrategyType: Periodical periodSeconds: 600 config: - ehpa.deployment-min-replicas: "1" - ehpa.fluctuation-threshold: "0" - ehpa.min-cpu-usage-threshold: "0" + replicas.fluctuation-threshold: "0" + replicas.min-cpu-usage-threshold: "0" + replicas.workload-min-replicas: "1" resourceSelectors: - apiVersion: apps/v1 kind: Deployment labelSelector: {} name: nginx-deployment - type: HPA + type: Replicas status: conditions: - - lastTransitionTime: "2022-05-15T13:34:19Z" + - lastTransitionTime: "2022-06-17T06:56:07Z" message: Analytics is ready reason: AnalyticsReady status: "True" type: Ready - lastUpdateTime: "2022-05-15T13:34:19Z" + lastUpdateTime: "2022-06-17T06:56:06Z" recommendations: - - lastStartTime: "2022-05-15T13:34:19Z" + - lastStartTime: "2022-06-17T06:56:06Z" message: Success - name: nginx-hpa-hpa-cd86s + name: nginx-replicas-replicas-wq6wm namespace: default targetRef: apiVersion: apps/v1 kind: Deployment name: nginx-deployment namespace: default - uid: b3cea8cb-259d-4cb2-bbbe-cd0e6544daaf + uid: 59f3eb3c-f786-4b15-b37e-774e5784c2db ``` ## 查看分析结果 @@ -105,62 +110,100 @@ status: 查看 **Recommendation** 结果： ```bash -kubectl get recommend nginx-hpa-hpa-cd86s -o yaml +kubectl get recommend -l analysis.crane.io/analytics-name=nginx-replicas -o yaml ``` 分析结果如下： +```yaml +apiVersion: v1 +items: + - apiVersion: analysis.crane.io/v1alpha1 + kind: Recommendation + metadata: + creationTimestamp: "2022-06-17T06:56:06Z" + generateName: nginx-replicas-replicas- + generation: 2 + labels: + analysis.crane.io/analytics-name: nginx-replicas + analysis.crane.io/analytics-type: Replicas + analysis.crane.io/analytics-uid: 795f245b-1e1f-4f7b-a02b-885d7a495e5b + app: nginx + name: nginx-replicas-replicas-wq6wm + namespace: default + ownerReferences: + - apiVersion: analysis.crane.io/v1alpha1 + blockOwnerDeletion: false + controller: false + kind: Analytics + name: nginx-replicas + uid: 795f245b-1e1f-4f7b-a02b-885d7a495e5b + resourceVersion: "2182455668" + selfLink: /apis/analysis.crane.io/v1alpha1/namespaces/default/recommendations/nginx-replicas-replicas-wq6wm + uid: 59f3eb3c-f786-4b15-b37e-774e5784c2db + spec: + adoptionType: StatusAndAnnotation + completionStrategy: + completionStrategyType: Once + targetRef: + apiVersion: apps/v1 + kind: Deployment + name: nginx-deployment + namespace: default + type: Replicas + status: + conditions: + - lastTransitionTime: "2022-06-17T06:56:07Z" + message: Recommendation is ready + reason: RecommendationReady + status: "True" + type: Ready + lastUpdateTime: "2022-06-17T06:56:07Z" + recommendedValue: | + effectiveHPA: + maxReplicas: 3 + metrics: + - resource: + name: cpu + target: + averageUtilization: 75 + type: Utilization + type: Resource + minReplicas: 3 + replicasRecommendation: + replicas: 3 +kind: List +metadata: + resourceVersion: "" + selfLink: "" +``` + +## 批量推荐 + +我们通过一个例子来演示如何使用 `Analytics` 推荐集群中所有的 Deployment 和 StatefulSet： + ```yaml apiVersion: analysis.crane.io/v1alpha1 -kind: Recommendation +kind: Analytics metadata: - creationTimestamp: "2022-05-15T13:34:19Z" - generateName: nginx-hpa-hpa- - generation: 2 - labels: - analysis.crane.io/analytics-name: nginx-hpa - analysis.crane.io/analytics-type: HPA - analysis.crane.io/analytics-uid: 5564edd0-d7cd-4da6-865b-27fa4fddf7c4 - app: nginx - name: nginx-hpa-hpa-cd86s - namespace: default - ownerReferences: - - apiVersion: analysis.crane.io/v1alpha1 - blockOwnerDeletion: false - controller: false - kind: Analytics - name: nginx-hpa - uid: 5564edd0-d7cd-4da6-865b-27fa4fddf7c4 + name: workload-replicas + namespace: crane-system # The Analytics in Crane-system will select all resource across all namespaces. spec: - adoptionType: StatusAndAnnotation + type: Replicas # This can only be "Resource" or "Replicas". completionStrategy: - completionStrategyType: Once - targetRef: - apiVersion: apps/v1 - kind: Deployment - name: nginx-deployment - namespace: default - type: HPA -status: - conditions: - - lastTransitionTime: "2022-05-15T13:34:19Z" - message: Recommendation is ready - reason: RecommendationReady - status: "True" - type: Ready - lastUpdateTime: "2022-05-15T13:34:19Z" - recommendedValue: | - maxReplicas: 2 - metrics: - - resource: - name: cpu - target: - averageUtilization: 75 - type: Utilization - type: Resource - minReplicas: 2 + completionStrategyType: Periodical # This can only be "Once" or "Periodical". + periodSeconds: 86400 # analytics selected resources every 1 day + resourceSelectors: # defines all the resources to be select with + - kind: Deployment + apiVersion: apps/v1 + - kind: StatefulSet + apiVersion: apps/v1 ``` +1. 当 namespace 等于 `crane-system` 时，`Analytics` 选择的资源是集群中所有的 namespace，当 namespace 不等于 `crane-system` 时，`Analytics` 选择 `Analytics` namespace 下的资源 +2. resourceSelectors 通过数组配置需要分析的资源，kind 和 apiVersion 是必填字段，name 选填 +3. resourceSelectors 支持配置任意支持 [Scale Subresource](https://kubernetes.io/docs/tasks/extend-kubernetes/custom-resources/custom-resource-definitions/#scale-subresource) 的资源 + ## 弹性推荐计算模型 ### 筛选阶段 @@ -243,3 +286,5 @@ status: | ehpa.min-cpu-target-utilization| 30 | | | ehpa.max-cpu-target-utilization| 75 | | | ehpa.reference-hpa| true | 继承现有的 HPA 配置 | + + diff --git a/docs/tutorials/resource-recommendation.md b/docs/tutorials/resource-recommendation.md index 037be4ab9..0f32811ec 100644 --- a/docs/tutorials/resource-recommendation.md +++ b/docs/tutorials/resource-recommendation.md @@ -6,10 +6,9 @@ Resource recommendation allows you to obtain recommended values for resources in Resource recommendations are a lightweight implementation of VPA and are more flexible. -1. Easy to install：as long as you install the Crane, it can be used -2. Algorithm: The algorithm model adopts the Moving Window algorithm of VPA, and supports to customization algo args , providing higher flexibility -3. Support batch analysis: With the ResourceSelector, users can batch analyze multiple workloads without creating VPA objects one by one -4. More portable: It is difficult to use VPA's Auto mode in production because it will cause container reconstruction when updating container resource configuration. Resource recommendation provides suggestions to users and leaves the decision of change to users +1. Algorithm: The algorithm model adopts the Moving Window algorithm of VPA, and supports to customization algo args , providing higher flexibility +2. Support batch analysis: With the ResourceSelector, users can batch analyze multiple workloads without creating VPA objects one by one +3. More portable: It is difficult to use VPA's Auto mode in production because it will cause container reconstruction when updating container resource configuration. Resource recommendation provides suggestions to users and leaves the decision of change to users ## Create Resource Analytics @@ -64,7 +63,7 @@ kubectl get analytics nginx-resource -o yaml The output is similar to: -```yaml hl_lines="27" +```yaml apiVersion: analysis.crane.io/v1alpha1 kind: Analytics metadata: @@ -111,7 +110,7 @@ kubectl get recommend -l analysis.crane.io/analytics-name=nginx-resource -o yaml The output is similar to: -```yaml hl_lines="32-37" +```yaml apiVersion: v1 items: - apiVersion: analysis.crane.io/v1alpha1 @@ -163,6 +162,33 @@ metadata: The `status.recommendedValue.ResourceRequest` is recommended by crane's recommendation engine. +## Batch recommendation + +Use a sample to show how to recommend all Deployments and StatefulSets by one `Analytics`: + +```yaml +apiVersion: analysis.crane.io/v1alpha1 +kind: Analytics +metadata: + name: workload-resource + namespace: crane-system # The Analytics in Crane-system will select all resource across all namespaces. +spec: + type: Resource # This can only be "Resource" or "Replicas". + completionStrategy: + completionStrategyType: Periodical # This can only be "Once" or "Periodical". + periodSeconds: 86400 # analytics selected resources every 1 day + resourceSelectors: # defines all the resources to be select with + - kind: Deployment + apiVersion: apps/v1 + - kind: StatefulSet + apiVersion: apps/v1 +``` + +1. when using `crane-system` as your namespace，`Analytics` selected all namespaces，when namespace not equal `crane-system`，`Analytics` selected the resource that in `Analytics` namespace +2. resourceSelectors defines the resource to analysis，kind and apiVersion is mandatory，name is optional +3. resourceSelectors supoort any resource that are [Scale Subresource](https://kubernetes.io/docs/tasks/extend-kubernetes/custom-resources/custom-resource-definitions/#scale-subresource) + + ## Resource Recommendation Algorithm model ### Inspecting diff --git a/docs/tutorials/resource-recommendation.zh.md b/docs/tutorials/resource-recommendation.zh.md index 47fad4a50..55799dbc3 100644 --- a/docs/tutorials/resource-recommendation.zh.md +++ b/docs/tutorials/resource-recommendation.zh.md @@ -1,15 +1,14 @@ # 资源推荐 -Kubernetes 的用户在创建应用资源时常常是基于经验值来设置 request 和 limit。通过资源推荐的算法分析应用的真实用量推荐更合适的资源配置，您可以参考并采纳它提升集群的资源利用率。 +Kubernetes 用户在创建应用资源时常常是基于经验值来设置 request 和 limit。通过资源推荐的算法分析应用的真实用量推荐更合适的资源配置，您可以参考并采纳它提升集群的资源利用率。 -## 资源推荐相比 VPA 的优势 +## 产品功能 资源推荐是 VPA 的轻量化实现，且更灵活。 -1. 安装方便：只要安装完 Crane 后即可使用 -2. 算法：算法模型采用了 VPA 的滑动窗口（Moving Window）算法，并且支持自定义算法的关键配置，提供了更高的灵活性 -3. 支持批量分析：通过 `Analytics` 的 ResourceSelector，用户可以批量分析多个工作负载，而无需一个一个的创建 VPA 对象 -4. 更轻便：由于 VPA 的 Auto 模式在更新容器资源配置时会导致容器重建，因此很难在生产上使用自动模式，资源推荐给用户提供资源建议，把变更的决定交给用户决定 +1. 算法：算法模型采用了 VPA 的滑动窗口（Moving Window）算法，并且支持自定义算法的关键配置，提供了更高的灵活性 +2. 支持批量分析：通过 `Analytics` 的 ResourceSelector，用户可以批量分析多个工作负载，而无需一个一个的创建 VPA 对象 +3. 更轻便：由于 VPA 的 Auto 模式在更新容器资源配置时会导致容器重建，因此很难在生产上使用自动模式，资源推荐给用户提供资源建议，把变更的决定交给用户决定 ## 创建资源分析 @@ -64,7 +63,7 @@ kubectl get analytics nginx-resource -o yaml 结果如下: -```yaml hl_lines="27" +```yaml apiVersion: analysis.crane.io/v1alpha1 kind: Analytics metadata: @@ -197,7 +196,11 @@ spec: 采用 VPA 的滑动窗口（Moving Window）算法分别计算每个容器的 CPU 和 Memory 并给出对应的推荐值 -## 资源推荐计算配置 +## 常见问题 + +### 如何让推荐结果更准确 + +应用在监控系统（比如 Prometheus）中的历史数据越久，推荐结果就越准确，建议生产上超过两周时间。对新建应用的预测往往不准，可以通过参数配置保证只对历史数据长度超过一定天数的业务推荐。 diff --git a/examples/analytics/analytics-workload-replicas.yaml b/examples/analytics/analytics-workload-replicas.yaml new file mode 100644 index 000000000..8ca996882 --- /dev/null +++ b/examples/analytics/analytics-workload-replicas.yaml @@ -0,0 +1,15 @@ +apiVersion: analysis.crane.io/v1alpha1 +kind: Analytics +metadata: + name: workload-replicas + namespace: crane-system # The Analytics in Crane-system will select all resource across all namespaces. +spec: + type: Replicas # This can only be "Resource" or "Replicas". + completionStrategy: + completionStrategyType: Periodical # This can only be "Once" or "Periodical". + periodSeconds: 86400 # analytics selected resources every 1 day + resourceSelectors: # defines all the resources to be select with + - kind: Deployment + apiVersion: apps/v1 + - kind: StatefulSet + apiVersion: apps/v1 diff --git a/mkdocs.yml b/mkdocs.yml index e6f1f1ae0..de6dc3e62 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -41,6 +41,11 @@ plugins: Introduction: 介绍 Installation: 安装 Tutorials: 教程 + Effective HPA: 智能水平弹性 + Analytics and Recommendation: 智能推荐 + Analytics Overview: 推荐总体介绍 + Resource Recommendation: 资源推荐 + Replicas Recommendation: 副本数推荐 Proposals: 提案 Contributing: 贡献 Mirror Repo: 镜像仓库 @@ -76,7 +81,7 @@ nav: - Tutorials: - Effective HPA: tutorials/using-effective-hpa-to-scaling-with-effectiveness.md - Analytics and Recommendation: - - Analytics: tutorials/analytics-and-recommendation.md + - Analytics Overview: tutorials/analytics-and-recommendation.md - Resource Recommendation: tutorials/resource-recommendation.md - Replicas Recommendation: tutorials/replicas-recommendation.md - Qos Ensurance: tutorials/using-qos-ensurance.md