deploy: 019e79a

blog/1/01/01/crane-v0.7通过控制台一键节省云成本/index.html

@@ -5,13 +5,13 @@
 资源推荐框架 Recommendation Framework Crane 的资源推荐，副本推荐功能在腾讯内部落地帮助自研业务每月节省了大量的成本，取得了很好的效果，详情请见：https://mp.weixin.qq.com/s/1SeMzcf_VRvRysZ9NLI-Sw 。同时，我们认为自动分析集群资源找到浪费并给出优化建议是帮助企业降本的重要方法，引入更多的分析类型至关重要。
 因此在 0.7.0 版本中，Crane 设计了 Recommendation Framework，它提供了一个可扩展的推荐框架以支持多种云资源的分析，并内置了多种推荐器：资源推荐，副本推荐，闲置资源推荐。Recommendation Framework 通过 RecommendationRule 和 Recommendation CRD 描述了如何进行资源的分析推荐。
 智能推荐的规则
-apiVersion: analysis.crane.io/v1alpha1 kind: RecommendationRule metadata: name: workloads-rule labels: analysis.crane.io/recommendation-rule-preinstall: &#34;true&#34; spec: runInterval: 24h # 每24h运行一次 resourceSelectors: # 资源的信息 - kind: Deployment apiVersion: apps/v1 - kind: StatefulSet apiVersion: apps/v1 namespaceSelector: any: true # 扫描所有namespace recommenders: # 使用 Workload 的副本和资源推荐器 - name: Replicas - name: Resource 推荐的结果"><meta property="og:type" content="article"><meta property="og:url" content="/blog/1/01/01/crane-v0.7%E9%80%9A%E8%BF%87%E6%8E%A7%E5%88%B6%E5%8F%B0%E4%B8%80%E9%94%AE%E8%8A%82%E7%9C%81%E4%BA%91%E6%88%90%E6%9C%AC/"><meta property="article:section" content="blog"><meta property="article:modified_time" content="2023-05-25T11:25:49+08:00"><meta property="og:site_name" content="Crane"><meta itemprop=name content="Crane v0.7：通过控制台一键节省云成本"><meta itemprop=description content="Crane（ Cloud Resource Analytics and Economics ） 是一个依托FinOps理论指导，基于云原生技术栈的云资源分析与成本优化平台。它的愿景是在保证客户应用运行质量的前提下实现极致的降本。
+apiVersion: analysis.crane.io/v1alpha1 kind: RecommendationRule metadata: name: workloads-rule labels: analysis.crane.io/recommendation-rule-preinstall: &#34;true&#34; spec: runInterval: 24h # 每24h运行一次 resourceSelectors: # 资源的信息 - kind: Deployment apiVersion: apps/v1 - kind: StatefulSet apiVersion: apps/v1 namespaceSelector: any: true # 扫描所有namespace recommenders: # 使用 Workload 的副本和资源推荐器 - name: Replicas - name: Resource 推荐的结果"><meta property="og:type" content="article"><meta property="og:url" content="/blog/1/01/01/crane-v0.7%E9%80%9A%E8%BF%87%E6%8E%A7%E5%88%B6%E5%8F%B0%E4%B8%80%E9%94%AE%E8%8A%82%E7%9C%81%E4%BA%91%E6%88%90%E6%9C%AC/"><meta property="article:section" content="blog"><meta property="article:modified_time" content="2023-05-25T11:26:16+08:00"><meta property="og:site_name" content="Crane"><meta itemprop=name content="Crane v0.7：通过控制台一键节省云成本"><meta itemprop=description content="Crane（ Cloud Resource Analytics and Economics ） 是一个依托FinOps理论指导，基于云原生技术栈的云资源分析与成本优化平台。它的愿景是在保证客户应用运行质量的前提下实现极致的降本。
 近期，Crane 发布了 0.7.0 版本。在新版本里我们提供了大量的新功能和优化，包括智能推荐框架 Recommendation Framework 以及全新改版的 Crane 产品化控制台。
 详细的 Release Note 请见：https://github.com/gocrane/crane/releases/tag/v0.7.0
 资源推荐框架 Recommendation Framework Crane 的资源推荐，副本推荐功能在腾讯内部落地帮助自研业务每月节省了大量的成本，取得了很好的效果，详情请见：https://mp.weixin.qq.com/s/1SeMzcf_VRvRysZ9NLI-Sw 。同时，我们认为自动分析集群资源找到浪费并给出优化建议是帮助企业降本的重要方法，引入更多的分析类型至关重要。
 因此在 0.7.0 版本中，Crane 设计了 Recommendation Framework，它提供了一个可扩展的推荐框架以支持多种云资源的分析，并内置了多种推荐器：资源推荐，副本推荐，闲置资源推荐。Recommendation Framework 通过 RecommendationRule 和 Recommendation CRD 描述了如何进行资源的分析推荐。
 智能推荐的规则
-apiVersion: analysis.crane.io/v1alpha1 kind: RecommendationRule metadata: name: workloads-rule labels: analysis.crane.io/recommendation-rule-preinstall: &#34;true&#34; spec: runInterval: 24h # 每24h运行一次 resourceSelectors: # 资源的信息 - kind: Deployment apiVersion: apps/v1 - kind: StatefulSet apiVersion: apps/v1 namespaceSelector: any: true # 扫描所有namespace recommenders: # 使用 Workload 的副本和资源推荐器 - name: Replicas - name: Resource 推荐的结果"><meta itemprop=dateModified content="2023-05-25T11:25:49+08:00"><meta itemprop=wordCount content="308"><meta itemprop=keywords content><meta name=twitter:card content="summary"><meta name=twitter:title content="Crane v0.7：通过控制台一键节省云成本"><meta name=twitter:description content="Crane（ Cloud Resource Analytics and Economics ） 是一个依托FinOps理论指导，基于云原生技术栈的云资源分析与成本优化平台。它的愿景是在保证客户应用运行质量的前提下实现极致的降本。
+apiVersion: analysis.crane.io/v1alpha1 kind: RecommendationRule metadata: name: workloads-rule labels: analysis.crane.io/recommendation-rule-preinstall: &#34;true&#34; spec: runInterval: 24h # 每24h运行一次 resourceSelectors: # 资源的信息 - kind: Deployment apiVersion: apps/v1 - kind: StatefulSet apiVersion: apps/v1 namespaceSelector: any: true # 扫描所有namespace recommenders: # 使用 Workload 的副本和资源推荐器 - name: Replicas - name: Resource 推荐的结果"><meta itemprop=dateModified content="2023-05-25T11:26:16+08:00"><meta itemprop=wordCount content="308"><meta itemprop=keywords content><meta name=twitter:card content="summary"><meta name=twitter:title content="Crane v0.7：通过控制台一键节省云成本"><meta name=twitter:description content="Crane（ Cloud Resource Analytics and Economics ） 是一个依托FinOps理论指导，基于云原生技术栈的云资源分析与成本优化平台。它的愿景是在保证客户应用运行质量的前提下实现极致的降本。
 近期，Crane 发布了 0.7.0 版本。在新版本里我们提供了大量的新功能和优化，包括智能推荐框架 Recommendation Framework 以及全新改版的 Crane 产品化控制台。
 详细的 Release Note 请见：https://github.com/gocrane/crane/releases/tag/v0.7.0
 资源推荐框架 Recommendation Framework Crane 的资源推荐，副本推荐功能在腾讯内部落地帮助自研业务每月节省了大量的成本，取得了很好的效果，详情请见：https://mp.weixin.qq.com/s/1SeMzcf_VRvRysZ9NLI-Sw 。同时，我们认为自动分析集群资源找到浪费并给出优化建议是帮助企业降本的重要方法，引入更多的分析类型至关重要。

docs/best-practices/effective-hpa-with-prometheus-adapter/index.html

@@ -1,7 +1,7 @@
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