Smart Recommendations

GreenKube’s recommendation engine analyzes your cluster metrics to produce actionable optimization suggestions that reduce both costs and carbon emissions simultaneously.

How It Works

The recommendation engine runs from the API, the startup scan, and the CLI. It reads historical metrics from the database over a configurable lookback window (RECOMMENDATION_LOOKBACK_DAYS, default: 7 days) and applies threshold-based detection algorithms across multiple dimensions.

Deployment-level deduplication: Pods belonging to the same Deployment are grouped together (via pod-name suffix pattern matching) so you receive one recommendation per workload — not one per replica.

Annual savings projection: Every recommendation includes estimated annual CO₂e savings (annual_co2e_savings_grams) and annual cost savings (annual_cost_savings_usd), extrapolated from the observation window.

9 Recommendation Types

🧟 Zombie Pod (ZOMBIE_POD)

What: Pods that are running and consuming resources but show near-zero energy usage — idle workloads that have been forgotten.

Detection: cost > ZOMBIE_COST_THRESHOLD AND energy < ZOMBIE_ENERGY_THRESHOLD

Scope: pod

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📏 CPU Rightsizing (RIGHTSIZING_CPU)

What: Pods with CPU requests significantly higher than actual utilization.

Detection: Average CPU utilization < RIGHTSIZING_CPU_THRESHOLD (default: 30%) of CPU request.

Output: Recommended new CPU request uses P95 usage, observed max, average usage, and headroom. Only reductions are surfaced — recommendations that would increase a request are discarded.

Scope: workload (grouped per Deployment)

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📏 Memory Rightsizing (RIGHTSIZING_MEMORY)

What: Pods with memory requests significantly higher than actual utilization.

Detection: Average memory utilization < RIGHTSIZING_MEMORY_THRESHOLD (default: 30%) of memory request.

Scope: workload (grouped per Deployment)

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📈 Autoscaling Candidate (AUTOSCALING_CANDIDATE)

What: Workloads with high CPU usage variability that would benefit from Horizontal Pod Autoscaling (HPA).

Detection:

• Coefficient of Variation > AUTOSCALING_CV_THRESHOLD (default: 0.7)
• Max/min ratio > AUTOSCALING_SPIKE_RATIO (default: 3.0)
• No existing HPA detected (via the HPACollector)

Scope: workload

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🌍 Carbon-Aware Scheduling (CARBON_AWARE_SCHEDULING)

What: Workloads running during high-carbon-intensity windows that could be shifted to cleaner periods.

Detection: Grid intensity during execution > zone average × CARBON_AWARE_THRESHOLD (default: 1.5×)

Scope: pod / workload

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🗂️ Idle Namespace (IDLE_NAMESPACE)

What: Namespaces with minimal energy consumption — likely forgotten resources still incurring cost.

Detection: Total namespace energy < IDLE_NAMESPACE_ENERGY_THRESHOLD

Scope: namespace

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🌙 Off-Peak Scaling (OFF_PEAK_SCALING)

What: Workloads that maintain full resource allocation during off-peak hours and could benefit from scheduled scale-down.

Detection: Sustained idle periods >= OFF_PEAK_MIN_IDLE_HOURS (default: 4h) during consistent time windows, below OFF_PEAK_IDLE_THRESHOLD of the daily peak.

Output: Suggested CronJob/KEDA schedule for scale-down + scale-up.

Scope: workload (grouped per Deployment)

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🖥️ Overprovisioned Node (OVERPROVISIONED_NODE)

What: Nodes with far more capacity than their scheduled workloads require.

Detection: Node CPU utilization < NODE_UTILIZATION_THRESHOLD (default: 20%)

Scope: node

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💤 Underutilized Node (UNDERUTILIZED_NODE)

What: Nodes running at very low CPU and memory utilization — consolidation candidates.

Detection: Node has fewer than 3 pods and average CPU utilization below 15%.

Scope: node

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Recommendation Lifecycle

Each recommendation persists in the database and is reconciled across scans:

active -> applied
      |
      +-> ignored -> active
      |
      +-> stale

Status	Meaning
active	Currently valid recommendation, shown in active lists and Grafana ranked cards
applied	Recommendation explicitly implemented through the API and included in realized savings
ignored	Recommendation intentionally hidden with an audit reason and restorable later
stale	Previously active recommendation that disappeared after refresh/reconciliation

Savings Ledger

When a recommendation is marked applied, GreenKube records realized annual savings and the SavingsAttributor converts them into per-period ledger rows. This drives two Prometheus gauges:

• greenkube_co2e_savings_attributed_grams_total — cumulative CO₂e savings
• greenkube_cost_savings_attributed_dollars_total — cumulative cost savings

These are displayed in the Grafana dashboard’s Impact Command Center section and on the web dashboard’s summary cards.

The new Actionable Recommendations Grafana row ranks the highest-impact active recommendations using:

• GET /api/v1/recommendations/top
• greenkube_top_recommendations

Accessing Recommendations

Web Dashboard

The /recommendations page provides:

• Active tab — current recommendations with type filter and annual savings preview
• Ignored tab — ignored recommendations with restore action
• Realized Savings tab — applied recommendations plus cumulative realized savings
• Ignore flow — ignore requires a reason

The dashboard currently exposes ignore and restore flows, but not an Apply button.

CLI

# View all recommendations (reads from database)
greenkube recommend

# Filter by namespace
greenkube recommend --namespace production

# Specify lookback window
greenkube recommend --last 7d

# Run live collection instead of reading from DB
greenkube recommend --live

# CI/CD gate: exit 1 if any recommendations exist
greenkube recommend --fail-on-recommendations

API

# Live recommendations (runs the recommender)
GET /api/v1/recommendations

# Persisted recommendations (optionally refresh first)
GET /api/v1/recommendations/active
GET /api/v1/recommendations/active?refresh=true

# Historical records
GET /api/v1/recommendations/history

# Savings summary
GET /api/v1/recommendations/savings

# Get applied recommendations
GET /api/v1/recommendations/applied

# Ranked active recommendations
GET /api/v1/recommendations/top?limit=5&metric=co2

# Update lifecycle status
PATCH /api/v1/recommendations/{id}/apply
PATCH /api/v1/recommendations/{id}/ignore
DELETE /api/v1/recommendations/{id}/ignore

Related

• Recommendations Guide — Step-by-step usage instructions
• Carbon Tracking — How emissions are estimated
• REST API — Full API reference