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Idle Resources

Part of: eks-cost-intelligence Purpose: Checks for zero-replica Deployments idle for extended periods, LoadBalancer Services with no healthy endpoints, namespaces with no running workloads but allocated quotas, orphaned ConfigMaps/Secrets not referenced by running workloads, and per-hour cost of unused load balancers

Overview

Idle resources is a mid-weight dimension (15 points max deduction). It detects resources that are provisioned and potentially incurring cost but serving no active workload — forgotten deployments scaled to zero, load balancers with no backends, empty namespaces with quotas, and orphaned configuration objects.

Unlike compute efficiency (which identifies under-utilized resources), this dimension targets resources that are doing nothing at all — pure waste with zero productive value.

Cost Reference: Unused Load Balancers

Load Balancer Type	Hourly Cost (idle)	Monthly Cost (idle)
Network Load Balancer (NLB)	~$0.0225/hr	~$16.43/month
Application Load Balancer (ALB)	~$0.0225/hr	~$16.43/month
Classic Load Balancer (CLB)	~$0.025/hr	~$18.25/month

Prices are US East (N. Virginia) baseline. Actual costs vary by region but the idle hourly rate applies even with zero traffic.

Checks Summary

#	Check	Default Threshold	Severity Logic
1	Zero-replica Deployments idle for extended periods	0 replicas for > 7 days	By count × resource allocation
2	LoadBalancer Services with no healthy endpoints	0 healthy endpoints	HIGH (Req 10.5) — per LB hourly cost
3	Namespaces with no running workloads but allocated quotas	0 running pods + active quota	MEDIUM
4	Orphaned ConfigMaps/Secrets not referenced by running workloads	Not mounted/envFrom by any running pod	LOW–MEDIUM by count

Pre-requisites

These checks require:

kubectl access to the cluster (for Deployments, Services, Endpoints, Namespaces, ConfigMaps, Secrets)
AWS CLI access for elasticloadbalancing:DescribeLoadBalancers, elasticloadbalancing:DescribeTargetHealth (optional — enriches LB cost data)
No metrics-server required — all checks use Kubernetes API state inspection only

Check 1: Zero-Replica Deployments Idle for Extended Periods

What it detects

Deployments that have been scaled to zero replicas and appear to have been in this state for an extended period (> 7 days). These represent forgotten or abandoned workloads that may still have associated resources (PVCs, ConfigMaps, Secrets, ServiceAccounts) consuming cluster overhead.

Data collection

Via kubectl:

# Find all Deployments with 0 replicas in non-system namespaces
kubectl get deployments --all-namespaces -o json | \
  jq -r '
    .items[] |
    select(.metadata.namespace | test("^kube-|^amazon-|^aws-") | not) |
    select(.spec.replicas == 0) |
    {
      namespace: .metadata.namespace,
      name: .metadata.name,
      replicas: .spec.replicas,
      last_updated: (.metadata.annotations["kubectl.kubernetes.io/last-applied-configuration"] // "unknown"),
      generation: .metadata.generation,
      conditions: [.status.conditions[]? | {type: .type, lastTransitionTime: .lastTransitionTime}]
    }'

# Check when the Deployment was last scaled (using events or annotations)
kubectl get events --all-namespaces --field-selector reason=ScalingReplicaSet \
  --sort-by='.lastTimestamp' -o json | \
  jq -r '
    .items[] |
    select(.involvedObject.kind == "Deployment") |
    select(.message | contains("Scaled down replica set") and contains(" to 0")) |
    "\(.involvedObject.namespace)/\(.involvedObject.name) scaled-to-zero at \(.lastTimestamp)"
  ' | tail -50

# Alternative: check last transition time on Available condition
kubectl get deployments --all-namespaces -o json | \
  jq -r '
    .items[] |
    select(.metadata.namespace | test("^kube-|^amazon-|^aws-") | not) |
    select(.spec.replicas == 0) |
    . as $dep |
    ($dep.status.conditions[]? | select(.type == "Available")) as $cond |
    "\($dep.metadata.namespace)/\($dep.metadata.name) | lastTransition=\($cond.lastTransitionTime // "unknown")"
  '

Via EKS MCP Server:

list_k8s_resources(
  cluster_name="<cluster>",
  kind="Deployment",
  api_version="apps/v1"
)
# Filter results for spec.replicas == 0
# Check status.conditions[].lastTransitionTime for idle duration

Analysis logic

For each Deployment in non-system namespaces:
  If spec.replicas == 0:
    idle_since = status.conditions["Available"].lastTransitionTime
    idle_days = (now - idle_since).days

    If idle_days > 7:
      → Generate finding
      
    # Estimate residual cost (associated resources still consuming)
    associated_pvcs = count PVCs referenced by the Deployment's pod template
    associated_secrets = count Secrets referenced by the Deployment's pod template
    residual_monthly_cost = sum(pvc_costs) + quota_overhead

Severity classification

Condition	Severity
Zero-replica Deployment idle > 30 days with associated PVCs	HIGH
Zero-replica Deployment idle > 30 days, no PVCs	MEDIUM
Zero-replica Deployment idle 7–30 days	LOW
Multiple (5+) zero-replica Deployments in same namespace	MEDIUM (governance gap)

Remediation

# Review and delete abandoned Deployments
kubectl delete deployment <name> -n <namespace>

# Or scale back up if it should be running
kubectl scale deployment <name> -n <namespace> --replicas=1

# List all associated resources before cleanup
kubectl get all,pvc,configmap,secret -n <namespace> -l app=<deployment-label>

# Bulk identification: list all zero-replica deploys older than 30 days
kubectl get deployments --all-namespaces -o json | \
  jq -r '
    .items[] |
    select(.metadata.namespace | test("^kube-|^amazon-|^aws-") | not) |
    select(.spec.replicas == 0) |
    select(.status.conditions[]? | 
      select(.type == "Available") |
      (.lastTransitionTime | fromdateiso8601) < (now - 2592000)) |
    "\(.metadata.namespace)/\(.metadata.name)"
  '

Check 2: LoadBalancer Services with No Healthy Endpoints

What it detects

Kubernetes Services of type LoadBalancer that have no healthy backend endpoints. These services provision AWS load balancers (NLB or ALB) that incur hourly charges (~$0.0225/hr for NLB, ~$0.0225/hr for ALB) even with zero traffic and no healthy targets.

Severity: HIGH (per Requirement 10.5) — Each idle load balancer costs ~$16.43/month minimum regardless of traffic.

Data collection

Via kubectl:

# Find all LoadBalancer Services
kubectl get services --all-namespaces -o json | \
  jq -r '
    .items[] |
    select(.spec.type == "LoadBalancer") |
    {
      namespace: .metadata.namespace,
      name: .metadata.name,
      lb_type: (if .metadata.annotations["service.beta.kubernetes.io/aws-load-balancer-type"] == "nlb" 
                or .metadata.annotations["service.beta.kubernetes.io/aws-load-balancer-type"] == "external"
                then "NLB" else "CLB/ALB" end),
      external_ip: (.status.loadBalancer.ingress[0].hostname // "pending"),
      selector: .spec.selector
    }'

# Check endpoints health for each LoadBalancer Service
kubectl get endpoints --all-namespaces -o json | \
  jq -r '
    .items[] |
    select(.subsets == null or .subsets == [] or 
           (.subsets[]?.addresses == null or .subsets[]?.addresses == [])) |
    {
      namespace: .metadata.namespace,
      name: .metadata.name,
      ready_addresses: 0,
      not_ready: ([.subsets[]?.notReadyAddresses[]?] | length)
    }'

# Combined: LoadBalancer Services with zero healthy endpoints
kubectl get services --all-namespaces -o json | \
  jq -r '
    .items[] |
    select(.spec.type == "LoadBalancer") |
    .metadata.namespace as $ns |
    .metadata.name as $name |
    "\($ns)/\($name)"
  ' | while read svc; do
    ns=$(echo "$svc" | cut -d'/' -f1)
    name=$(echo "$svc" | cut -d'/' -f2)
    ready=$(kubectl get endpoints "$name" -n "$ns" -o json 2>/dev/null | \
      jq '[.subsets[]?.addresses[]?] | length')
    if [ "${ready:-0}" -eq 0 ]; then
      echo "IDLE_LB: $svc (0 healthy endpoints)"
    fi
  done

Via AWS CLI (enriches with actual LB details):

# List ELBv2 load balancers and check target group health
aws elbv2 describe-load-balancers --query 'LoadBalancers[*].{
  Name:LoadBalancerName,
  ARN:LoadBalancerArn,
  Type:Type,
  State:State.Code,
  DNSName:DNSName
}' --output table

# For each LB, check target group health
aws elbv2 describe-target-groups \
  --load-balancer-arn <lb-arn> \
  --query 'TargetGroups[*].TargetGroupArn' --output text | \
  while read tg_arn; do
    echo "Target Group: $tg_arn"
    aws elbv2 describe-target-health \
      --target-group-arn "$tg_arn" \
      --query 'TargetHealthDescriptions[*].{Target:Target.Id,Health:TargetHealth.State}'
  done

# List Classic Load Balancers and check instance health
aws elb describe-load-balancers --query 'LoadBalancerDescriptions[*].{
  Name:LoadBalancerName,
  Instances:Instances[*].InstanceId
}' --output json | \
  jq '.[] | select(.Instances == null or (.Instances | length) == 0) | .Name'

Via EKS MCP Server:

list_k8s_resources(
  cluster_name="<cluster>",
  kind="Service",
  api_version="v1"
)
# Filter for spec.type == "LoadBalancer"
# Then check endpoints for each:

list_k8s_resources(
  cluster_name="<cluster>",
  kind="Endpoints",
  api_version="v1",
  namespace="<namespace>"
)
# Check subsets[].addresses length for each matching endpoint

Analysis logic

For each Service with spec.type == "LoadBalancer":
  endpoints = get Endpoints object with same name/namespace
  healthy_count = count(endpoints.subsets[].addresses[])

  If healthy_count == 0:
    # Determine LB type from annotations
    lb_type = annotations["service.beta.kubernetes.io/aws-load-balancer-type"]
    
    If lb_type in ["nlb", "external", "nlb-ip"]:
      hourly_cost = 0.0225  # NLB idle cost
    Else if lb_type == "alb" or ingress-controller managed:
      hourly_cost = 0.0225  # ALB idle cost
    Else:
      hourly_cost = 0.025   # CLB idle cost (legacy)
    
    monthly_cost = hourly_cost × 730
    → Generate HIGH severity finding (per Req 10.5)

Severity classification

Condition	Severity
Any LoadBalancer Service with 0 healthy endpoints	HIGH
Multiple idle LoadBalancers (3+)	HIGH (compounding waste)

Per Requirement 10.5: Idle LoadBalancer Services always generate HIGH severity findings because they represent direct, ongoing AWS billing with zero value.

Per-hour cost breakdown

LB Type	Hourly Idle Cost	Monthly Idle Cost	Annual Idle Cost
NLB	~$0.0225/hr	~$16.43/month	~$197.10/year
ALB	~$0.0225/hr	~$16.43/month	~$197.10/year
CLB	~$0.025/hr	~$18.25/month	~$219.00/year

These are base charges. LCU/NLCU charges are zero when there is no traffic, but the hourly fee applies regardless.

Remediation

# Delete the idle LoadBalancer Service (removes AWS LB)
kubectl delete service <name> -n <namespace>

# Or switch to ClusterIP if the LB is no longer needed externally
kubectl patch service <name> -n <namespace> -p '{"spec": {"type": "ClusterIP"}}'

# Verify the AWS LB was actually deleted after Service removal
aws elbv2 describe-load-balancers --query 'LoadBalancers[*].LoadBalancerName'

# If the Service should have backends, check why pods are unhealthy
kubectl get pods -n <namespace> -l <service-selector-labels>
kubectl describe endpoints <service-name> -n <namespace>

Check 3: Namespaces with No Running Workloads but Allocated Quotas

What it detects

Namespaces that have ResourceQuotas or LimitRanges defined but contain no running pods. This indicates abandoned environments that still reserve cluster capacity through quotas or create governance overhead.

Data collection

Via kubectl:

# Find namespaces with ResourceQuotas
kubectl get resourcequotas --all-namespaces -o json | \
  jq -r '.items[] | "\(.metadata.namespace)"' | sort -u

# Find namespaces with zero running pods
kubectl get pods --all-namespaces --field-selector=status.phase=Running -o json | \
  jq -r '[.items[].metadata.namespace] | unique[]' > /tmp/active_namespaces.txt

kubectl get namespaces -o json | \
  jq -r '.items[].metadata.name' | \
  grep -v -F -f /tmp/active_namespaces.txt | \
  grep -v -E "^(kube-|amazon-|aws-|default$)"

# Combined: namespaces with quotas but no running workloads
kubectl get namespaces -o json | \
  jq -r '
    .items[] |
    select(.metadata.name | test("^kube-|^amazon-|^aws-|^default$") | not) |
    .metadata.name
  ' | while read ns; do
    pod_count=$(kubectl get pods -n "$ns" --field-selector=status.phase=Running \
      --no-headers 2>/dev/null | wc -l)
    quota_count=$(kubectl get resourcequotas -n "$ns" --no-headers 2>/dev/null | wc -l)
    limitrange_count=$(kubectl get limitranges -n "$ns" --no-headers 2>/dev/null | wc -l)
    
    if [ "$pod_count" -eq 0 ] && [ "$((quota_count + limitrange_count))" -gt 0 ]; then
      echo "EMPTY_NS_WITH_QUOTA: $ns (quotas=$quota_count, limitranges=$limitrange_count, running_pods=0)"
    fi
  done

# Get quota details for empty namespaces (shows reserved capacity)
kubectl get resourcequotas -n <namespace> -o json | \
  jq '.items[] | {
    name: .metadata.name,
    hard: .spec.hard,
    used: .status.used
  }'

Via EKS MCP Server:

list_k8s_resources(
  cluster_name="<cluster>",
  kind="Namespace",
  api_version="v1"
)

list_k8s_resources(
  cluster_name="<cluster>",
  kind="ResourceQuota",
  api_version="v1",
  namespace="<namespace>"
)

list_k8s_resources(
  cluster_name="<cluster>",
  kind="Pod",
  api_version="v1",
  namespace="<namespace>"
)
# Check for running pods; if zero, flag namespace with quotas

Analysis logic

For each non-system namespace:
  running_pods = count pods with status.phase == "Running"
  has_quota = (ResourceQuotas exist in namespace)
  has_limitrange = (LimitRanges exist in namespace)

  If running_pods == 0 AND (has_quota OR has_limitrange):
    # Calculate reserved capacity from quota spec
    reserved_cpu = quota.spec.hard["requests.cpu"]
    reserved_mem = quota.spec.hard["requests.memory"]
    
    → Generate finding
    # Note: quotas don't directly consume resources, but indicate 
    # governance overhead and often mean the namespace was intended for workloads

Severity classification

Condition	Severity
Empty namespace with quota + no pods for > 30 days	MEDIUM
Empty namespace with quota + associated PVCs still bound	HIGH
Empty namespace with quota only (no PVCs)	LOW–MEDIUM
Multiple (3+) empty namespaces with quotas	MEDIUM (governance gap)

Remediation

# Review and delete empty namespaces (CAUTION: deletes all resources in namespace)
kubectl delete namespace <namespace>

# Or remove just the quota if the namespace may be reused
kubectl delete resourcequota <quota-name> -n <namespace>
kubectl delete limitrange <limitrange-name> -n <namespace>

# Before deleting, check for any non-running resources (PVCs, Secrets, etc.)
kubectl get all,pvc,configmap,secret,serviceaccount -n <namespace>

Check 4: Orphaned ConfigMaps and Secrets Not Referenced by Running Workloads

What it detects

ConfigMaps and Secrets that exist in non-system namespaces but are not referenced by any running pod — either as volume mounts, environment variable sources (envFrom/valueFrom), or projected volumes. These represent configuration debt and potential security exposure (orphaned secrets).

Data collection

Via kubectl:

# Step 1: Get all ConfigMaps/Secrets referenced by running pods
kubectl get pods --all-namespaces -o json | \
  jq -r '
    .items[] |
    select(.status.phase == "Running") |
    select(.metadata.namespace | test("^kube-|^amazon-|^aws-") | not) |
    .metadata.namespace as $ns |
    (
      # Volume mounts (configMap and secret)
      ([.spec.volumes[]? | 
        select(.configMap) | "\($ns)/configmap/\(.configMap.name)"] +
       [.spec.volumes[]? | 
        select(.secret) | "\($ns)/secret/\(.secret.secretName)"] +
       [.spec.volumes[]? | 
        select(.projected) | .projected.sources[]? |
        (if .configMap then "\($ns)/configmap/\(.configMap.name)"
         elif .secret then "\($ns)/secret/\(.secret.name)"
         else empty end)] +
      # EnvFrom references
       [.spec.containers[]?.envFrom[]? |
        (if .configMapRef then "\($ns)/configmap/\(.configMapRef.name)"
         elif .secretRef then "\($ns)/secret/\(.secretRef.name)"
         else empty end)] +
      # Individual env valueFrom references
       [.spec.containers[]?.env[]?.valueFrom? |
        (if .configMapKeyRef then "\($ns)/configmap/\(.configMapKeyRef.name)"
         elif .secretKeyRef then "\($ns)/secret/\(.secretKeyRef.name)"
         else empty end)]
    )[]
  ' | sort -u > /tmp/referenced_configs.txt

# Step 2: Get all ConfigMaps in non-system namespaces (excluding system ones)
kubectl get configmaps --all-namespaces -o json | \
  jq -r '
    .items[] |
    select(.metadata.namespace | test("^kube-|^amazon-|^aws-") | not) |
    select(.metadata.name | test("^kube-root-ca") | not) |
    "\(.metadata.namespace)/configmap/\(.metadata.name)"
  ' | sort > /tmp/all_configmaps.txt

# Step 3: Get all Secrets in non-system namespaces (excluding service account tokens)
kubectl get secrets --all-namespaces -o json | \
  jq -r '
    .items[] |
    select(.metadata.namespace | test("^kube-|^amazon-|^aws-") | not) |
    select(.type != "kubernetes.io/service-account-token") |
    select(.metadata.name | test("^default-token-") | not) |
    "\(.metadata.namespace)/secret/\(.metadata.name)"
  ' | sort > /tmp/all_secrets.txt

# Step 4: Find orphaned ConfigMaps (not referenced by any running pod)
comm -23 /tmp/all_configmaps.txt /tmp/referenced_configs.txt

# Step 5: Find orphaned Secrets (not referenced by any running pod)
comm -23 /tmp/all_secrets.txt /tmp/referenced_configs.txt

# Simplified single-command approach (summary by namespace)
kubectl get pods --all-namespaces -o json | \
  jq -r '
    [.items[] |
     select(.status.phase == "Running") |
     select(.metadata.namespace | test("^kube-|^amazon-|^aws-") | not) |
     .metadata.namespace as $ns |
     (
       [.spec.volumes[]?.configMap?.name // empty] +
       [.spec.volumes[]?.secret?.secretName // empty] +
       [.spec.containers[]?.envFrom[]?.configMapRef?.name // empty] +
       [.spec.containers[]?.envFrom[]?.secretRef?.name // empty] +
       [.spec.containers[]?.env[]?.valueFrom?.configMapKeyRef?.name // empty] +
       [.spec.containers[]?.env[]?.valueFrom?.secretKeyRef?.name // empty]
     )[] | {ns: $ns, ref: .}
    ] | group_by(.ns) | map({namespace: .[0].ns, referenced: [.[].ref] | unique})
  '

Via EKS MCP Server:

# Get all pods to build reference map
list_k8s_resources(
  cluster_name="<cluster>",
  kind="Pod",
  api_version="v1"
)
# Parse spec.volumes, spec.containers[].envFrom, spec.containers[].env[].valueFrom
# to build set of referenced ConfigMaps and Secrets

# Get all ConfigMaps
list_k8s_resources(
  cluster_name="<cluster>",
  kind="ConfigMap",
  api_version="v1",
  namespace="<namespace>"
)

# Get all Secrets
list_k8s_resources(
  cluster_name="<cluster>",
  kind="Secret",
  api_version="v1",
  namespace="<namespace>"
)
# Diff: all - referenced = orphaned

Exclusions

Always exclude from orphan detection:

ConfigMaps named kube-root-ca.crt (system-managed)
Secrets of type kubernetes.io/service-account-token (auto-created)
Secrets matching ^default-token- pattern (legacy SA tokens)
ConfigMaps/Secrets in system namespaces (kube-*, amazon-*, aws-*)
Helm release secrets (type helm.sh/release.v1) — these track Helm state
ConfigMaps with label app.kubernetes.io/managed-by: Helm that match a deployed release

Analysis logic

For each non-system namespace:
  referenced_cms = set of ConfigMap names used by running pods (volumes, envFrom, valueFrom)
  referenced_secrets = set of Secret names used by running pods (volumes, envFrom, valueFrom)
  
  all_cms = set of all ConfigMaps in namespace (excluding system CMs)
  all_secrets = set of all Secrets in namespace (excluding SA tokens, Helm releases)
  
  orphaned_cms = all_cms - referenced_cms
  orphaned_secrets = all_secrets - referenced_secrets
  
  If len(orphaned_cms) + len(orphaned_secrets) > 0:
    → Generate finding
    # Secrets carry more risk (potential credential exposure)
    # ConfigMaps are lower risk (just configuration debt)

Severity classification

Condition	Severity
> 10 orphaned Secrets in a namespace	MEDIUM
Orphaned Secrets (any count)	LOW
> 20 orphaned ConfigMaps in a namespace	MEDIUM
Orphaned ConfigMaps (any count)	LOW
Total orphaned objects across cluster > 50	MEDIUM (cluster hygiene)

Note: Orphaned ConfigMaps/Secrets have minimal direct cost impact but indicate configuration debt, potential security exposure (stale credentials in Secrets), and complicate cluster operations.

Remediation

# Review orphaned ConfigMaps before deleting
kubectl get configmap <name> -n <namespace> -o yaml

# Delete confirmed orphaned ConfigMaps
kubectl delete configmap <name> -n <namespace>

# Delete confirmed orphaned Secrets (review first!)
kubectl get secret <name> -n <namespace> -o yaml | head -20  # Check type and annotations
kubectl delete secret <name> -n <namespace>

# Bulk cleanup: delete all orphaned ConfigMaps in a namespace (use with caution)
# First generate the list, review it, then delete
comm -23 /tmp/all_configmaps.txt /tmp/referenced_configs.txt | \
  grep "^<namespace>/" | \
  sed 's|.*/configmap/||' | \
  xargs -I{} kubectl delete configmap {} -n <namespace> --dry-run=client

# Remove --dry-run=client after confirming the list is correct

Scoring Contribution

The idle resources dimension has a maximum deduction of 15 points.

Deduction calculation

deduction = 0

For each finding in this dimension:
  If severity == CRITICAL: deduction += 15 × 0.6 = 9.0
  If severity == HIGH:     deduction += 15 × 0.3 = 4.5
  If severity == MEDIUM:   deduction += 15 × 0.15 = 2.25
  If severity == LOW:      deduction += 15 × 0.05 = 0.75

actual_deduction = min(deduction, 15)  # Cap at maximum

Dimension status

Condition	Status
All checks completed	ASSESSED
Some checks partially completed	ASSESSED (with note)
No Kubernetes API access	Cannot assess — skill halts at pre-flight

This dimension is never SKIPPED because all checks require only the Kubernetes API (no metrics sources needed).

Decision Tree

START
  │
  ├─ Check 1: Zero-replica Deployments
  │   ├─ List all Deployments with spec.replicas == 0
  │   ├─ Check idle duration via status.conditions[].lastTransitionTime
  │   ├─ If idle > 7 days → Generate finding (severity by duration + PVCs)
  │   └─ Check for associated PVCs still bound
  │
  ├─ Check 2: Idle LoadBalancer Services
  │   ├─ List all Services with spec.type == "LoadBalancer"
  │   ├─ For each, check Endpoints object for healthy addresses
  │   ├─ If 0 healthy endpoints → Generate HIGH severity finding
  │   ├─ Calculate per-hour cost: NLB ~$0.0225/hr, ALB ~$0.0225/hr
  │   └─ Optionally verify via AWS ELBv2 API for target group health
  │
  ├─ Check 3: Empty Namespaces with Quotas
  │   ├─ List namespaces with ResourceQuotas or LimitRanges
  │   ├─ For each, count running pods
  │   ├─ If 0 running pods + quota exists → Generate finding
  │   └─ Check for bound PVCs in empty namespace (escalates severity)
  │
  ├─ Check 4: Orphaned ConfigMaps/Secrets
  │   ├─ Build reference map from all running pods
  │   ├─ List all ConfigMaps/Secrets (excluding system objects)
  │   ├─ Diff: all - referenced = orphaned
  │   ├─ Exclude Helm releases, SA tokens, system CMs
  │   └─ Generate finding if orphan count exceeds threshold
  │
  └─ Aggregate findings → Calculate dimension deduction (max 15 pts)

Examples

Example Finding: Idle Load Balancer

finding:
  id: "idle-lb-staging-api-gateway"
  dimension: "idle"
  severity: "HIGH"
  affected_resource: "staging/api-gateway (Service type=LoadBalancer)"
  current_state: "NLB provisioned with 0 healthy endpoints for 14 days"
  monthly_cost: 16.43
  monthly_waste: 16.43
  monthly_savings: 16.43
  effort: "Low"
  fix_summary: "Delete unused LoadBalancer Service or fix backend pods"
  remediation: "kubectl delete service api-gateway -n staging"
  confidence: "high"
  data_sources: ["kubernetes_api", "aws_elb_api"]

Example Finding: Zero-Replica Deployment

finding:
  id: "idle-deploy-dev-old-backend"
  dimension: "idle"
  severity: "MEDIUM"
  affected_resource: "dev/old-backend (Deployment, 0 replicas since 2024-08-15)"
  current_state: "Scaled to 0 replicas for 45 days, 2 PVCs still bound (50Gi total)"
  monthly_cost: 5.00
  monthly_waste: 5.00
  monthly_savings: 5.00
  effort: "Low"
  fix_summary: "Delete abandoned deployment and associated PVCs"
  remediation: "kubectl delete deployment old-backend -n dev && kubectl delete pvc old-backend-data-0 old-backend-data-1 -n dev"
  confidence: "medium"
  data_sources: ["kubernetes_api"]

Example Finding: Orphaned Secrets

finding:
  id: "idle-orphaned-secrets-production"
  dimension: "idle"
  severity: "LOW"
  affected_resource: "production namespace (8 orphaned Secrets)"
  current_state: "8 Secrets not referenced by any running pod"
  monthly_cost: 0
  monthly_waste: 0
  monthly_savings: 0
  effort: "Low"
  fix_summary: "Review and delete orphaned Secrets to reduce configuration debt"
  remediation: "Review each Secret and delete if no longer needed"
  confidence: "medium"
  data_sources: ["kubernetes_api"]

Overview​

Cost Reference: Unused Load Balancers​

Checks Summary​

Pre-requisites​

Check 1: Zero-Replica Deployments Idle for Extended Periods​

What it detects​

Data collection​

Analysis logic​

Severity classification​

Remediation​

Check 2: LoadBalancer Services with No Healthy Endpoints​

What it detects​

Data collection​

Analysis logic​

Severity classification​

Per-hour cost breakdown​

Remediation​

Check 3: Namespaces with No Running Workloads but Allocated Quotas​

What it detects​

Data collection​

Analysis logic​

Severity classification​

Remediation​

Check 4: Orphaned ConfigMaps and Secrets Not Referenced by Running Workloads​

What it detects​

Data collection​

Exclusions​

Analysis logic​

Severity classification​

Remediation​

Scoring Contribution​

Deduction calculation​

Dimension status​

Decision Tree​

Examples​

Example Finding: Idle Load Balancer​

Example Finding: Zero-Replica Deployment​

Example Finding: Orphaned Secrets​

Overview

Cost Reference: Unused Load Balancers

Checks Summary

Pre-requisites

Check 1: Zero-Replica Deployments Idle for Extended Periods

What it detects

Data collection

Analysis logic

Severity classification

Remediation

Check 2: LoadBalancer Services with No Healthy Endpoints

What it detects

Data collection

Analysis logic

Severity classification

Per-hour cost breakdown

Remediation

Check 3: Namespaces with No Running Workloads but Allocated Quotas

What it detects

Data collection

Analysis logic

Severity classification

Remediation

Check 4: Orphaned ConfigMaps and Secrets Not Referenced by Running Workloads

What it detects

Data collection

Exclusions

Analysis logic

Severity classification

Remediation

Scoring Contribution

Deduction calculation

Dimension status

Decision Tree

Examples

Example Finding: Idle Load Balancer

Example Finding: Zero-Replica Deployment

Example Finding: Orphaned Secrets