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Author: rootsongjc

content/blog/introducing-kube-scheduler-simulator/index.md

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+---
+title: "kube-scheduler-simulator：让调度器可观察、可调试、可扩展"
+summary: "本文介绍了 kube-scheduler-simulator，它是 Kubernetes 调度器的模拟器，能暴露调度器内部决策，用于测试调度约束、配置和自定义插件，解决调度器测试难题，仅需安装 Docker 即可使用，由 Kubernetes SIG Scheduling 开发并欢迎社区参与。"
+authors: ["Kensei Nakada"]
+translators: ["云原生社区"]
+categories: ["Kubernetes"]
+tags: ["Kuberentes"]
+draft: false
+date: 2025-04-08T14:35:56+08:00
+links:
+  - icon: language
+    icon_pack: fa
+    name: 阅读英文版原文
+    url: https://kubernetes.io/blog/2025/04/07/introducing-kube-scheduler-simulator/
+---
+
+Kubernetes Scheduler 是控制面中的核心组件之一，负责决定每个 Pod 运行在哪个节点上。换句话说，所有使用 Kubernetes 的用户，其 Pod 的命运都掌握在调度器手中。
+
+[kube-scheduler-simulator](https://github.com/kubernetes-sigs/kube-scheduler-simulator) 是一个 Kubernetes 调度器的“模拟器”，最初是我（Kensei Nakada）在 [Google Summer of Code 2021](https://summerofcode.withgoogle.com/) 中启动的项目，后续得到了众多贡献者的支持。这个工具的目标是帮助用户深入观察调度器的行为和决策逻辑。
+
+无论你是使用诸如 [Pod 亲和性](https://kubernetes.io/zh-cn/docs/concepts/scheduling-eviction/assign-pod-node/#affinity-and-anti-affinity) 等调度约束的普通用户，还是开发自定义调度插件的调度器专家，这款模拟器都将成为你理解和测试调度策略的重要助手。
+
+## 为什么我们需要一个调度器模拟器？
+
+Kubernetes Scheduler 本质上是一个插件驱动的“黑盒”，每个插件从不同的角度参与调度决策，理解它的行为并不容易。
+
+即使你在测试集群中看到 Pod 正常调度，也不能保证它是按你预期的逻辑调度的。这种“表面正常，实则偏差”的问题，往往会在生产环境中带来意想不到的调度后果。
+
+此外，调度器的测试也非常具有挑战性。真实集群中的调度场景复杂多样，无法通过有限的测试用例全面覆盖。即使是 Kubernetes 官方的 upstream 调度器，也经常是在发布后才被用户发现问题。
+
+虽然开发环境或沙箱集群是常用的测试手段，但这类环境通常规模较小、工作负载有限，与实际生产集群的行为存在巨大差异。因此，仅靠传统方法，很难预知调度器在真实环境中的表现。
+
+kube-scheduler-simulator 诞生正是为了填补这一空白：
+
+- 用户可以验证调度约束、调度器配置、自定义插件的行为；
+- 可以在模拟环境中测试调度效果，而不影响实际的工作负载；
+- 还能观察调度过程中的每一个决策细节，真正“把调度器变成白盒”。
+
+## 模拟器具有什么能力？
+
+kube-scheduler-simulator 的核心能力是：**揭示调度器内部的决策过程**。Kubernetes Scheduler 是基于 [调度框架（Scheduling Framework）](https://kubernetes.io/zh-cn/docs/concepts/scheduling-eviction/scheduling-framework/) 实现的，整个调度流程分为多个扩展点，如 Filter、Score、Bind 等，每个阶段会调用对应插件进行判断和打分。
+
+模拟器提供了图形化界面，用户可以在其中创建 Pod、Node、Deployment 等 Kubernetes 资源，并清晰地看到调度器每个插件的执行结果和得分情况。
+
+![调度器模拟器 Web UI](simulator.png)
+
+在这个模拟器中，我们运行的是一个 **可调试版本的调度器**，它会将各插件在不同阶段的执行结果写入 Pod 的注解中，前端界面再将这些信息进行可视化展示。
+
+例如，你可以查看调度器在 Filter 阶段如何判断某个节点是否可用，在 Score 阶段各插件打了多少分，最终选择了哪个节点作为目标节点。这些信息都能在 Pod 的注解中查阅：
+
+```yaml
+kind: Pod
+apiVersion: v1
+metadata:
+  # The JSONs within these annotations are manually formatted for clarity in the blog post. 
+  annotations:
+    kube-scheduler-simulator.sigs.k8s.io/bind-result: '{"DefaultBinder":"success"}'
+    kube-scheduler-simulator.sigs.k8s.io/filter-result: >-
+      {
+        "node-jjfg5":{
+            "NodeName":"passed",
+            "NodeResourcesFit":"passed",
+            "NodeUnschedulable":"passed",
+            "TaintToleration":"passed"
+        },
+        "node-mtb5x":{
+            "NodeName":"passed",
+            "NodeResourcesFit":"passed",
+            "NodeUnschedulable":"passed",
+            "TaintToleration":"passed"
+        }
+      }      
+    kube-scheduler-simulator.sigs.k8s.io/finalscore-result: >-
+      {
+        "node-jjfg5":{
+            "ImageLocality":"0",
+            "NodeAffinity":"0",
+            "NodeResourcesBalancedAllocation":"52",
+            "NodeResourcesFit":"47",
+            "TaintToleration":"300",
+            "VolumeBinding":"0"
+        },
+        "node-mtb5x":{
+            "ImageLocality":"0",
+            "NodeAffinity":"0",
+            "NodeResourcesBalancedAllocation":"76",
+            "NodeResourcesFit":"73",
+            "TaintToleration":"300",
+            "VolumeBinding":"0"
+        }
+      }       
+    kube-scheduler-simulator.sigs.k8s.io/permit-result: '{}'
+    kube-scheduler-simulator.sigs.k8s.io/permit-result-timeout: '{}'
+    kube-scheduler-simulator.sigs.k8s.io/postfilter-result: '{}'
+    kube-scheduler-simulator.sigs.k8s.io/prebind-result: '{"VolumeBinding":"success"}'
+    kube-scheduler-simulator.sigs.k8s.io/prefilter-result: '{}'
+    kube-scheduler-simulator.sigs.k8s.io/prefilter-result-status: >-
+      {
+        "AzureDiskLimits":"",
+        "EBSLimits":"",
+        "GCEPDLimits":"",
+        "InterPodAffinity":"",
+        "NodeAffinity":"",
+        "NodePorts":"",
+        "NodeResourcesFit":"success",
+        "NodeVolumeLimits":"",
+        "PodTopologySpread":"",
+        "VolumeBinding":"",
+        "VolumeRestrictions":"",
+        "VolumeZone":""
+      }      
+    kube-scheduler-simulator.sigs.k8s.io/prescore-result: >-
+      {
+        "InterPodAffinity":"",
+        "NodeAffinity":"success",
+        "NodeResourcesBalancedAllocation":"success",
+        "NodeResourcesFit":"success",
+        "PodTopologySpread":"",
+        "TaintToleration":"success"
+      }      
+    kube-scheduler-simulator.sigs.k8s.io/reserve-result: '{"VolumeBinding":"success"}'
+    kube-scheduler-simulator.sigs.k8s.io/result-history: >-
+      [
+        {
+            "kube-scheduler-simulator.sigs.k8s.io/bind-result":"{\"DefaultBinder\":\"success\"}",
+            "kube-scheduler-simulator.sigs.k8s.io/filter-result":"{\"node-jjfg5\":{\"NodeName\":\"passed\",\"NodeResourcesFit\":\"passed\",\"NodeUnschedulable\":\"passed\",\"TaintToleration\":\"passed\"},\"node-mtb5x\":{\"NodeName\":\"passed\",\"NodeResourcesFit\":\"passed\",\"NodeUnschedulable\":\"passed\",\"TaintToleration\":\"passed\"}}",
+            "kube-scheduler-simulator.sigs.k8s.io/finalscore-result":"{\"node-jjfg5\":{\"ImageLocality\":\"0\",\"NodeAffinity\":\"0\",\"NodeResourcesBalancedAllocation\":\"52\",\"NodeResourcesFit\":\"47\",\"TaintToleration\":\"300\",\"VolumeBinding\":\"0\"},\"node-mtb5x\":{\"ImageLocality\":\"0\",\"NodeAffinity\":\"0\",\"NodeResourcesBalancedAllocation\":\"76\",\"NodeResourcesFit\":\"73\",\"TaintToleration\":\"300\",\"VolumeBinding\":\"0\"}}",
+            "kube-scheduler-simulator.sigs.k8s.io/permit-result":"{}",
+            "kube-scheduler-simulator.sigs.k8s.io/permit-result-timeout":"{}",
+            "kube-scheduler-simulator.sigs.k8s.io/postfilter-result":"{}",
+            "kube-scheduler-simulator.sigs.k8s.io/prebind-result":"{\"VolumeBinding\":\"success\"}",
+            "kube-scheduler-simulator.sigs.k8s.io/prefilter-result":"{}",
+            "kube-scheduler-simulator.sigs.k8s.io/prefilter-result-status":"{\"AzureDiskLimits\":\"\",\"EBSLimits\":\"\",\"GCEPDLimits\":\"\",\"InterPodAffinity\":\"\",\"NodeAffinity\":\"\",\"NodePorts\":\"\",\"NodeResourcesFit\":\"success\",\"NodeVolumeLimits\":\"\",\"PodTopologySpread\":\"\",\"VolumeBinding\":\"\",\"VolumeRestrictions\":\"\",\"VolumeZone\":\"\"}",
+            "kube-scheduler-simulator.sigs.k8s.io/prescore-result":"{\"InterPodAffinity\":\"\",\"NodeAffinity\":\"success\",\"NodeResourcesBalancedAllocation\":\"success\",\"NodeResourcesFit\":\"success\",\"PodTopologySpread\":\"\",\"TaintToleration\":\"success\"}",
+            "kube-scheduler-simulator.sigs.k8s.io/reserve-result":"{\"VolumeBinding\":\"success\"}",
+            "kube-scheduler-simulator.sigs.k8s.io/score-result":"{\"node-jjfg5\":{\"ImageLocality\":\"0\",\"NodeAffinity\":\"0\",\"NodeResourcesBalancedAllocation\":\"52\",\"NodeResourcesFit\":\"47\",\"TaintToleration\":\"0\",\"VolumeBinding\":\"0\"},\"node-mtb5x\":{\"ImageLocality\":\"0\",\"NodeAffinity\":\"0\",\"NodeResourcesBalancedAllocation\":\"76\",\"NodeResourcesFit\":\"73\",\"TaintToleration\":\"0\",\"VolumeBinding\":\"0\"}}",
+            "kube-scheduler-simulator.sigs.k8s.io/selected-node":"node-mtb5x"
+        }
+      ]      
+    kube-scheduler-simulator.sigs.k8s.io/score-result: >-
+      {
+        "node-jjfg5":{
+            "ImageLocality":"0",
+            "NodeAffinity":"0",
+            "NodeResourcesBalancedAllocation":"52",
+            "NodeResourcesFit":"47",
+            "TaintToleration":"0",
+            "VolumeBinding":"0"
+        },
+        "node-mtb5x":{
+            "ImageLocality":"0",
+            "NodeAffinity":"0",
+            "NodeResourcesBalancedAllocation":"76",
+            "NodeResourcesFit":"73",
+            "TaintToleration":"0",
+            "VolumeBinding":"0"
+        }
+      }      
+    kube-scheduler-simulator.sigs.k8s.io/selected-node: node-mtb5x
+```
+
+不仅如此，用户还可以集成自己开发的 [调度插件](https://kubernetes.io/zh-cn/docs/concepts/scheduling-eviction/scheduling-framework/) 或 [调度扩展器（Extender）](https://github.com/kubernetes/design-proposals-archive/blob/main/scheduling/scheduler_extender.md)，并在模拟器中观察其行为，调试效果显著提升。
+
+该可调试调度器还可以脱离模拟器独立运行，用于集成测试、自定义调度器的开发或真实集群中的调试分析。
+
+## 模拟器 = 更好的开发环境？
+
+如前所述，真实调度场景复杂多样，单靠开发集群无法覆盖全部可能性。模拟器提供了一个更强大的方案：
+
+通过使用 [集群资源导入功能](https://github.com/kubernetes-sigs/kube-scheduler-simulator/blob/master/simulator/docs/import-cluster-resources.md)，用户可以将生产集群的资源同步到模拟器中，**在不影响实际业务的前提下测试新版本调度器**。
+
+你可以在模拟器中验证 Pod 的调度行为是否符合预期，再将调度器部署到生产集群，极大降低了调度相关的变更风险。
+
+## 使用场景总结
+
+1. **集群用户**：验证调度约束（如 PodAffinity、TopologySpread）是否按预期工作；
+2. **集群管理员**：评估调度器配置变更对调度结果的影响；
+3. **调度器插件开发者**：在模拟器中测试自定义插件、使用同步功能进行更真实的验证。
+
+## 如何上手？
+
+这个项目无需 Kubernetes 集群，只需要本地安装 Docker 即可：
+
+```bash
+git clone git@github.com:kubernetes-sigs/kube-scheduler-simulator.git
+cd kube-scheduler-simulator
+make docker_up
+```
+
+默认情况下，Web 界面将运行在 `http://localhost:3000`，即可开始你的调度实验！
+
+👉 项目地址：https://github.com/kubernetes-sigs/kube-scheduler-simulator
+
+## 如何参与贡献？
+
+该项目由 [Kubernetes SIG Scheduling](https://github.com/kubernetes/community/blob/master/sig-scheduling/README.md#kube-scheduler-simulator) 维护。欢迎你提出 Issues、提交 PR，也欢迎加入社区参与讨论。
+
+Slack 频道：[#sig-scheduling](https://kubernetes.slack.com/messages/sig-scheduling)
+
+## 鸣谢
+
+这个模拟器的发展离不开众多志愿者工程师的坚持和贡献，感谢所有为之付出心力的 [贡献者们](https://github.com/kubernetes-sigs/kube-scheduler-simulator/graphs/contributors)！
+