Kubernetes Autoscaling with Middleware: Monitor, Optimize, and Troubleshoot Workloads

Kubernetes autoscaling helps applications respond to changing demand by adjusting workload replicas and infrastructure capacity. However, enabling autoscaling does not necessarily mean applications will run efficiently or infrastructure costs will decrease.

An application might scale from five to twenty Pods during a traffic spike but continue experiencing latency because its database is overloaded. Another workload might reserve significantly more CPU and memory than it consumes, preventing Kubernetes from scheduling additional Pods on existing nodes.

These problems occur because scaling decisions depend on resource requests, workload metrics, application behavior, and available cluster capacity.

Middleware helps engineering teams understand these relationships through unified Kubernetes monitoring, application performance monitoring, custom metrics, and AI-assisted incident investigation.

With Middleware’s KEDA external scaler, teams can also use supported metrics stored in Middleware to drive Kubernetes scaling decisions.

This guide explains how to monitor Kubernetes autoscaling, identify inefficient resource allocations, investigate scaling failures, and improve workload performance using Middleware.

Understanding Kubernetes Autoscaling

Kubernetes provides several mechanisms for automatically adjusting application resources according to demand.

Horizontal Pod Autoscaler (HPA) changes the number of application replicas based on metrics such as CPU utilization, memory consumption, or custom application measurements.

Vertical Pod Autoscaler (VPA) recommends or adjusts the CPU and memory requests assigned to containers, depending on its configuration.

Node autoscalers, including Cluster Autoscaler and Karpenter, adjust infrastructure capacity when additional nodes are required, or existing capacity can be consolidated.

These components address different aspects of Kubernetes scaling.

A production environment may use several of these mechanisms simultaneously.

For example, HPA may request ten additional application replicas. If existing nodes lack sufficient resources, a node autoscaler can provision additional capacity.

Middleware provides visibility into the resource consumption and workload behavior surrounding these operations, helping teams determine whether scaling decisions are achieving the expected results.

Why Kubernetes Autoscaling Can Lead to Resource Waste

One of the biggest challenges in Kubernetes resource management is the difference between resources requested by applications and the resources they actually consume.

Kubernetes uses resource requests when determining where Pods can be scheduled.

If applications request significantly more CPU and memory than they need, cluster capacity can appear fully allocated even when actual resource consumption is relatively low.

Consider a Kubernetes worker node with 8 vCPUs.

Three applications might collectively request 7 CPU cores while actually consuming only 2 cores.

Although the node has substantial unused processing capacity, Kubernetes cannot necessarily schedule another workload requesting 2 CPU cores because the available schedulable CPU has already been allocated.

This can result in additional node provisioning despite low actual utilization.

The problem becomes more significant when autoscaling adds additional replicas with unnecessarily large resource requests.

How Resource Requests Affect HPA

CPU-based HPA calculates utilization relative to configured CPU requests.

For example, suppose a container requests:

resources:
  requests:
    cpu: "2000m"

If the container consumes 600 millicores, its CPU utilization relative to the request is approximately 30%.

An HPA configured with a target of 70% will not recommend scaling up based on that value alone.

However, if the application is experiencing increased request latency because of I/O waits or connection limits, CPU-based scaling may not reflect the actual demand.

This illustrates why autoscaling performance depends on both resource configuration and the metrics used for scaling.

For more information, read Kubernetes Metrics: Best Practices for Effective Monitoring.

How Middleware Helps Optimize Kubernetes Autoscaling

Middleware provides observability across Kubernetes infrastructure, application workloads, and related telemetry.

This helps engineering teams evaluate scaling behavior and investigate resource inefficiencies without switching between multiple disconnected monitoring systems.

1. Monitor Resource Requests and Actual Utilization

Middleware provides Kubernetes resource telemetry that helps teams analyze CPU and memory consumption.

The goal is to understand whether resource requests reflect actual application requirements.

For example, an application may consistently consume 200 millicores of CPU while requesting 1,000 millicores.

This difference does not automatically mean the request should be reduced. The application may experience significant traffic spikes or require additional capacity during startup.

However, it provides a useful starting point for investigating resource allocation.

Teams should compare normal utilization, peak consumption, and application performance before adjusting resource requests.

For resource-efficiency analysis, Middleware’s Kubernetes telemetry includes utilization measurements such as:

The availability and exact representation of these measurements should be confirmed in your Middleware environment.

Important: A high CPU-limit utilization ratio is not itself proof of CPU throttling. Throttling should be confirmed using appropriate throttling metrics. Similarly, high memory-limit utilization indicates risk but does not prove that a container has been OOMKilled.

2. Monitor Pod Scaling and Node Capacity

When Kubernetes increases application replicas, new Pods require sufficient CPU and memory capacity.

If the cluster cannot accommodate them, Pods may remain Pending until additional resources become available.

Middleware’s Kubernetes monitoring provides visibility into node and workload resource consumption.

For example, if HPA requests additional Pods during a traffic spike, engineers can compare node utilization and workload health to understand whether capacity constraints are developing.

Kubernetes scheduling events provide the specific reasons why a Pod cannot be placed.

This combination helps distinguish an application-level scaling problem from an infrastructure capacity problem.

3. Use Custom Metrics to Improve Scaling Decisions

For workloads that do not correlate well with CPU or memory utilization, Middleware can provide application-specific scaling metrics through its KEDA external scaler.

For example, a RabbitMQ worker can scale according to queue backlog instead of CPU utilization.

Similarly, an application can use a supported custom metric representing requests or workload demand.

Middleware provides the metric source, KEDA exposes the scaling signal, and Kubernetes HPA adjusts replicas.

For the complete configuration, see our companion guide: Scaling Kubernetes Workloads on Custom Metrics with Middleware.

4. Investigate Scaling Failures with OpsAI

Kubernetes scaling problems frequently involve multiple interconnected components.

For example, HPA may increase replicas while application memory consumption rises and several new Pods begin restarting.

Determining the root cause requires examining resource metrics, application logs, Kubernetes events, and relevant traces.

Middleware OpsAI supports AI-assisted root cause analysis for Kubernetes and application incidents.

OpsAI can help engineers identify likely causes and recommend remediation actions using available observability context.

For supported Kubernetes incidents, Middleware also provides Auto RCA and Auto Fix capabilities.

These features should be configured with appropriate permissions and operational safeguards.

OpsAI assists with incident investigation and remediation; it does not replace Kubernetes’ scaling controllers.

Step-by-Step: Monitor Kubernetes Autoscaling with Middleware

The following walkthrough demonstrates how to connect a Kubernetes cluster to Middleware and evaluate the performance of autoscaled workloads.

Step 1: Install the Middleware Kubernetes Agent

First, sign in to your Middleware account.

Open the installation section and select Kubernetes.

Before installing the agent, verify the active cluster:

kubectl config current-context

Next, confirm that Kubernetes nodes are accessible:

kubectl get nodes

Add the Middleware Helm repository:

helm repo add middleware-labs https://helm.middleware.io
helm repo update middleware-labs

Install the core Kubernetes monitoring components:

helm install mw-agent middleware-labs/mw-kube-agent-v3 \
  --set global.mw.apiKey=<MW_API_KEY> \
  --set global.mw.target=https://<MW_UID>.middleware.io:443 \
  --set global.clusterMetadata.name=<CLUSTER_NAME> \
  -n mw-agent-ns \
  --create-namespace

Replace the placeholders with the values from your Middleware account.

For production installations, use Middleware’s recommended Secret configuration rather than embedding credentials in shared deployment scripts.

The agent deploys a DaemonSet for node-level telemetry and Deployment components for cluster-wide monitoring.

Optional application auto-instrumentation and OpsAI components can be enabled through the Helm configuration.

For detailed setup instructions, see the Middleware Kubernetes Agent documentation.

Step 2: Verify Monitoring Components

Check whether the Kubernetes monitoring components are running.

kubectl get pods -n mw-agent-ns

Verify the core DaemonSet and Deployment:

kubectl get daemonset mw-kube-agent \
  -n mw-agent-ns
kubectl get deployment mw-kube-agent \
  -n mw-agent-ns

Once the components are available, open Middleware and select your Kubernetes cluster.

You should be able to inspect node health, workload information, and resource utilization.

Suggested screenshot: Middleware Kubernetes cluster overview showing node CPU, memory, workload status, and resource utilization.

Step 3: Establish Workload Performance Baselines

Before changing autoscaling configurations, analyze how applications perform under normal conditions.

Review CPU and memory consumption across application Pods, including peak usage rather than only averages.

Also examine application latency, throughput, error rates, and container restart patterns when those signals are collected.

For example, an application may typically consume 40% CPU during normal traffic but reach 85% during peak periods.

Understanding these patterns helps engineers select appropriate HPA targets and determine whether existing resource requests are reasonable.

Middleware’s dashboards provide historical resource context that can help evaluate these trends.

Step 4: Configure Horizontal Pod Autoscaler

The following configuration demonstrates a CPU-based HPA for an API deployment.

Create a file named api-service-hpa.yaml.

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler

metadata:
  name: api-service-hpa
  namespace: production

spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: api-service

  minReplicas: 2
  maxReplicas: 15

  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 70

  behavior:
    scaleDown:
      stabilizationWindowSeconds: 300

This configuration targets 70% CPU utilization relative to configured CPU requests.

Kubernetes can adjust the Deployment between two and fifteen replicas.

The five-minute scale-down stabilization window helps avoid unnecessary replica reductions following temporary traffic decreases.

Ensure that the application containers have suitable CPU requests and that the Kubernetes resource metrics pipeline is available.

Apply the configuration:

kubectl apply -f api-service-hpa.yaml

Verify:

kubectl get hpa -n production

Inspect its status and recent events:

kubectl describe hpa api-service-hpa \
  -n production

Step 5: Evaluate Scaling Performance in Middleware

Once autoscaling is enabled, observe how the application responds to changing traffic.

Use Kubernetes to monitor HPA:

kubectl get hpa -n production -w

Then inspect application and infrastructure metrics in Middleware.

A useful evaluation compares:

For example, if replica count doubles but application latency remains elevated, additional investigation may be required.

Middleware can help identify whether the issue involves application behavior or infrastructure pressure.

For exact scaling events, use HPA status, Kubernetes events, or separately collected autoscaler metrics.

Step 6: Configure Alerts

Once the workload is visible in Middleware, configure alerts for conditions that may indicate autoscaling problems.

Useful alerts include sustained CPU pressure, high memory utilization, repeated container restarts, and workload unavailability.

If HPA-specific metrics are collected, teams can also alert when desired replicas reach the configured maximum or when scaling metrics become unavailable.

The purpose is to identify when scaling stops maintaining application performance.

For more information, see Monitoring Kubernetes Applications with Middleware.

How to Identify Overprovisioned Kubernetes Workloads

Overprovisioning happens when workloads reserve more computing capacity than they need for their expected operating conditions.

This can reduce scheduling efficiency and increase infrastructure costs.

However, resource optimization requires more than simply reducing CPU and memory requests.

Compare Resource Requests with Consumption

Review configured resource requests against actual usage over a representative period.

For example, suppose an application requests 1,000 millicores of CPU but usually consumes only 150 millicores.

This difference may indicate an opportunity for optimization.

However, check peak usage, latency, and application startup behavior before changing requests.

Evaluate Minimum Replica Counts

Applications sometimes maintain more replicas than required during low-traffic periods.

If a workload consistently operates at low utilization, review its minimum HPA replica count.

Reducing the minimum may be appropriate for some applications, provided availability requirements and startup latency can still be met.

Review Node Scheduling Efficiency

When resource requests are unnecessarily high, Kubernetes may require additional nodes even when actual utilization is low.

Compare workload requests with available node capacity.

If appropriate, adjust resource requests gradually and observe the effects on scheduling and application performance.

Node consolidation and infrastructure cost savings depend on the configured node autoscaler and available instance capacity.

Monitor the Results

After changing resource allocations, continue monitoring CPU utilization, memory consumption, application latency, and container failures.

A configuration that reduces resource requests but increases application errors is not a successful optimization.

Middleware provides the observability context needed to evaluate these trade-offs.

Common Kubernetes Autoscaling Problems and How to Troubleshoot Them

Autoscaling failures can occur because of missing metrics, incorrect resource settings, insufficient infrastructure capacity, or application-level bottlenecks.

Scenario 1: HPA Adds Replicas, but Application Latency Remains High

Suppose Kubernetes increases an API deployment from five to fifteen replicas during peak traffic.

CPU utilization decreases, but users continue experiencing slow responses.

Middleware’s distributed tracing can help investigate whether database queries or downstream dependencies are responsible.

If a shared database is saturated, adding more replicas may increase connection pressure without improving performance.

The appropriate solution may involve database optimization, connection pooling, or changes to workload concurrency.

Scenario 2: New Pods Remain Pending

HPA requests additional replicas, but Kubernetes cannot schedule them.

Check the affected Pods:

kubectl get pods -n production

Inspect the scheduling events:

kubectl describe pod <POD_NAME> \
  -n production

Common causes include insufficient CPU or memory, resource quotas, and node scheduling constraints.

Middleware’s infrastructure monitoring helps engineers investigate related node resource pressure.

Scenario 3: Pods Restart During Traffic Spikes

An application may scale successfully but experience repeated container restarts.

This can occur because of memory exhaustion, application errors, or startup failures.

Check container restart counts, termination reasons, logs, and memory consumption.

Middleware provides centralized monitoring context, while OpsAI can assist with root cause investigation for supported incidents.

For additional guidance, see Diagnosing Kubernetes Workload Issues.

Scenario 4: HPA Frequently Scales Up and Down

Rapid scaling changes can occur when metrics fluctuate around the configured target.

This behavior may increase resource churn and create unnecessary application startup overhead.

Review HPA scaling policies, metric aggregation windows, and stabilization settings.

For example:

behavior:
  scaleDown:
    stabilizationWindowSeconds: 300

This helps Kubernetes avoid immediately reducing replicas after short decreases in observed demand.

Kubernetes Autoscaling Troubleshooting Checklist

How Middleware OpsAI Supports Kubernetes Troubleshooting

Kubernetes incidents rarely involve a single isolated metric.

For example, a sudden traffic spike can trigger additional replicas, increase database connections, and produce resource pressure across several services.

Investigating the sequence may require examining Kubernetes events, application traces, container logs, and infrastructure metrics.

Middleware OpsAI brings AI-assisted investigation into this workflow.

For supported Kubernetes issues, OpsAI can analyze available telemetry, identify likely root causes, and suggest remediation.

Middleware supports two relevant investigation approaches.

Auto RCA helps engineers understand the suspected cause of an incident and review proposed remediation actions.

Auto Fix can apply supported Kubernetes remediations automatically when the capability is enabled and appropriately configured.

For application code issues, OpsAI can also generate pull requests for review, depending on the integration and incident type.

This helps reduce manual investigation while keeping Kubernetes autoscaling and incident remediation as separate responsibilities.

Learn more about Middleware OpsAI SRE Agent.

Best Practices for Optimizing Kubernetes Autoscaling

1. Establish performance baselines. Understand normal and peak resource consumption before configuring scaling thresholds.

2. Select suitable scaling metrics. CPU and memory work for some workloads, while queue depth, request rate, and concurrency may be more appropriate for others.

3. Avoid conflicting autoscalers. Ensure HPA, VPA, and node autoscaling configurations do not make incompatible changes to the same workloads.

4. Right-size resource requests carefully. Evaluate historical consumption, peak demand, startup behavior, and application performance before reducing resource allocations.

5. Monitor downstream dependencies. More application replicas can increase pressure on databases, caches, and external APIs.

6. Use safe scaling policies. Configure realistic replica limits, stabilization windows, and application readiness checks.

7. Investigate scaling outcomes. Evaluate application latency, error rates, and infrastructure utilization after scaling events.

Why Middleware for Kubernetes Autoscaling?

Middleware connects the metrics that influence scaling decisions with the broader observability data required to understand their effects.

For engineering teams, this reduces the gap between configuring an autoscaler and understanding whether it is working effectively.

Instead of treating Kubernetes scaling as an isolated infrastructure operation, teams can evaluate replica changes alongside application reliability, resource efficiency, and downstream dependencies.

CTA: Optimize Kubernetes Autoscaling with Middleware

Summary

Kubernetes autoscaling helps applications respond to changing demand, but effective scaling depends on accurate metrics, appropriate resource requests, sufficient node capacity, and application performance.

Incorrect configurations can lead to unnecessary replicas, inefficient resource allocation, and scaling failures.

Middleware helps teams investigate these problems by providing Kubernetes monitoring, application performance visibility, custom metrics, and AI-assisted troubleshooting.

Its KEDA external scaler also enables supported Middleware metrics to participate directly in Kubernetes scaling decisions.

By combining native Kubernetes autoscaling with Middleware’s observability capabilities, engineering teams can make more informed decisions about workload capacity, troubleshoot failures, and improve infrastructure efficiency.

Frequently Asked Questions