Kubernetes Fundamentals and Cluster Operations — Lesson 3: Volumes, persistent volumes, claims, storage classes, scheduling, taints, tolerations, affinity, requests, limits, autoscaling, and disruption budgets

Learning Objectives

• Translate a realistic infrastructure or application requirement into an executable approach for Volumes, persistent volumes, claims, storage classes, scheduling, taints, tolerations, affinity, requests, limits, autoscaling, and disruption budgets.
• Use stateful workload validation and scheduling analysis to verify configuration, connectivity, security, and expected behavior.
• Explain how to recover safely when a pod remaining pending because resource requests exceed every eligible node occurs.

Detailed Topics

• Volumes
• Persistent volumes
• Claims
• Storage classes
• Scheduling
• Taints
• Tolerations
• Affinity
• Requests
• Limits
• Autoscaling
• And disruption budgets
• Decision ownership, prerequisites, and acceptance evidence for Volumes, persistent volumes, claims, storage classes, scheduling, taints, tolerations, affinity, requests, limits, autoscaling, and disruption budgets
• Relevant access, security, capacity, dependency, performance, recovery, and support considerations

Instructor Demonstration

The instructor builds a representative scenario for Volumes, persistent volumes, claims, storage classes, scheduling, taints, tolerations, affinity, requests, limits, autoscaling, and disruption budgets, narrating each decision and showing how it contributes to a maintainable Kubernetes cluster practice covering container fundamentals, workload deployment, networking, storage, security, observability, upgrades, and incident handling. The demonstration records stateful workload validation and scheduling analysis so learners can compare design intent with observable behavior.

Hands-On Exercise

Configure and test Volumes, persistent volumes, claims, storage classes, scheduling, taints, tolerations, affinity, requests, limits, autoscaling, and disruption budgets with representative infrastructure or application data. Use “a pod remaining pending because resource requests exceed every eligible node” as the troubleshooting challenge, compare hypotheses, retest the affected path, and complete stateful workload validation and scheduling analysis for review.

1. Define the requirement and identify dependencies before changing the environment.
2. Create a baseline result that can be compared with later changes.
3. Reproduce the exception and test one hypothesis at a time.
4. Complete the evidence record and obtain peer or instructor review.

Scenario Analysis

• Clarify the service trigger, affected components and roles, prerequisites, and acceptable result for Volumes, persistent volumes, claims, storage classes, scheduling, taints, tolerations, affinity, requests, limits, autoscaling, and disruption budgets.
• Compare at least two implementation or correction choices and identify which evidence would distinguish them.
• Decide how stateful workload validation and scheduling analysis should be reviewed, approved, retained, and handed to the next owner.

Troubleshooting Guidance

Avoid changing multiple settings at once while troubleshooting Volumes, persistent volumes, claims, storage classes, scheduling, taints, tolerations, affinity, requests, limits, autoscaling, and disruption budgets. Preserve the failing example, review stateful workload validation and scheduling analysis, and verify whether “a pod remaining pending because resource requests exceed every eligible node” is the root cause. After remediation, confirm downstream effects and add a monitoring action.

Validation Checklist

• The scope of Volumes, persistent volumes, claims, storage classes, scheduling, taints, tolerations, affinity, requests, limits, autoscaling, and disruption budgets is clear, including what is deliberately excluded from the test.
• Evidence in stateful workload validation and scheduling analysis supports each material decision and outcome.
• A negative test demonstrates that controls reject or expose an invalid condition.
• Open risks, owners, due actions, and production safeguards are documented.

Lesson Outcome

Learners can apply Volumes, persistent volumes, claims, storage classes, scheduling, taints, tolerations, affinity, requests, limits, autoscaling, and disruption budgets, explain the decisions made, and defend the result with stateful workload validation and scheduling analysis.