How I Built Self‑Healing Kubernetes Platforms (and Cut On‑Call by 35%)

Source: Dev.to

Why Most Kubernetes Clusters Still Depend on Humans

In many teams, Kubernetes looks automated — but when nodes get saturated, reality kicks in:

• Someone gets paged at 2 AM
• They SSH or kubectl into the cluster
• Cordon the node
• Drain workloads
• Hope autoscaling or Karpenter replaces it correctly

This manual loop repeats dozens of times a month in high‑traffic environments.

When I work with teams, this is usually the moment I ask:

Why is a human still doing deterministic infrastructure work?

That question led to building a self‑healing node remediation platform using Kubernetes Operators, Prometheus intelligence, and Karpenter.

The Platform Engineering Approach (Not Just DevOps Scripts)

Instead of wiring alerts to shell scripts, I approached this as a platform problem:

• The system must be stateful
• It must enforce guardrails
• It must be auditable
• It must integrate cleanly with Kubernetes primitives

That’s why the solution is built as a Kubernetes Operator, not a cron job or webhook glue.

What the Platform Does

The platform continuously evaluates real node health, not just kubelet conditions.

Signals used

• CPU saturation over time
• Memory pressure
• Disk exhaustion
• Pod eviction storms

All signals come from Prometheus metrics, which provide far richer context than node conditions alone.

Architecture Overview

flowchart TD
    Prometheus --> Alertmanager --> NodeRemediationOperator
    NodeRemediationOperator -->|cordon node| Node
    NodeRemediationOperator -->|drain workloads safely| Node
    NodeRemediationOperator -->|delete node| Node
    NodeRemediationOperator -->|Karpenter provisions replacement| Karpenter

Why an Operator Instead of Automation Scripts

An operator provides:

• Rate‑limited remediation (avoid cascading failures)
• Cooldown windows between actions
• Policy‑driven behaviour via CRDs
• Declarative safety controls
• Status visibility inside the cluster

Everything is Kubernetes‑native and observable.

Safety First: Production Guardrails

Auto‑remediation without safety is just chaos engineering. The platform enforces:

• Max remediations per hour
• Mandatory cooldowns
• PodDisruptionBudget awareness
• Label‑based opt‑in (remediable=true)
• Dry‑run mode for new clusters

This allows teams to trust automation, not fear it.

What Happens When a Node Is Saturated

1. Prometheus detects sustained saturation
2. Alertmanager notifies the operator
3. Operator validates policy and cooldowns
4. Node is cordoned
5. Workloads are drained safely
6. Node is deleted
7. Karpenter provisions fresh capacity

No SSH. No runbooks. No humans.

Measurable Business Impact

This wasn’t achieved by adding more engineers — it was achieved by building a better platform.

Why This Matters for Engineering Teams

The pattern scales across:

• EKS, GKE, AKS
• Stateless and stateful workloads
• Regulated and high‑availability environments

It shifts teams from reactive operations to intent‑driven infrastructure.

How This Fits into a Larger Platform

The operator is usually deployed alongside:

• GitOps pipelines (ArgoCD / Flux)
• Terraform‑based cluster provisioning
• SLO‑driven alerting
• Developer self‑service templates
• Cost‑aware autoscaling

Together, they form a self‑service internal platform — not just a collection of tools.

Want Something Like This in Your Cluster?

If your team:

• Runs Kubernetes at scale
• Still handles node issues manually
• Wants fewer pages and higher reliability

I help teams design and implement production‑grade platform automation — from operators to internal developer platforms.

Reach out if you want to discuss:

• Kubernetes operators
• EKS platform architecture
• Auto‑remediation & self‑healing systems
• Platform engineering best practices

aws #kubernetes #platform-engineering #devops #karpenter

Automation should reduce human stress — not increase it. 🚀