AIOps Engine - ML-Powered Alert Intelligence

Machine learning-powered operations intelligence for anomaly detection, predictive alerting, alert correlation, and automated remediation.

Overview

The AIOps Engine transforms alert management from reactive firefighting to proactive intelligence. By applying ML to operational data, we reduce alert fatigue, predict issues before they impact users, and automate resolution of known problems.

Business Impact

Metric	Before AIOps	After AIOps	Improvement
MTTA (Acknowledge)	5-10 min	Under 1 min	80%+
False Positive Rate	15-20%	Under 5%	70%+
L1 Auto-Resolved	0%	40%+	New capability
Alert Noise	100%	30%	70% reduction

---

AIOps Capabilities

1. Anomaly Detection

Automatically detect unusual patterns in metrics without manual threshold configuration.

Detection Methods:

Method	Use Case	How It Works
Z-Score	Simple thresholds	Statistical deviation from mean
Isolation Forest	Multivariate	Outlier detection across dimensions
Prophet	Seasonal patterns	Facebook's time series forecasting
ARIMA	Trend analysis	Autoregressive modeling

Example: CPU Anomaly Detection

# The AIOps engine learns normal patterns
baseline = {
    "metric": "cpu_usage",
    "mean": 45.2,
    "std": 8.3,
    "seasonal_pattern": "higher weekday lunch",
    "learning_window": "30 days"
}

# When current value deviates significantly
current_value = 92.5
z_score = (92.5 - 45.2) / 8.3  # = 5.7

# Result: Anomaly detected (z > 3)
alert = {
    "type": "anomaly",
    "metric": "cpu_usage",
    "severity": "P2",
    "message": "CPU usage 5.7 standard deviations above normal",
    "baseline": 45.2,
    "current": 92.5
}

2. Predictive Alerting

Forecast issues before they occur by analyzing trends.

Prediction Types:

Prediction	Lead Time	Accuracy
Disk Full	4-24 hours	92%
Memory Exhaustion	1-4 hours	88%
Certificate Expiry	7-30 days	99%
SLO Burn Rate	1-6 hours	85%
Capacity Limits	1-7 days	82%

Example: Disk Space Prediction

{
  "prediction": {
    "metric": "disk_usage",
    "current_value": 0.85,
    "trend": "increasing",
    "rate": "2GB/hour",
    "predicted_full": "2026-01-25T01:00:00Z",
    "hours_until_full": 4,
    "confidence": 0.92
  },
  "alert": {
    "type": "prediction",
    "severity": "P2",
    "message": "Disk predicted to be full in 4 hours at current rate",
    "suggested_action": "Increase disk size or clean up logs"
  }
}

3. Alert Correlation

Group related alerts to identify root causes and reduce noise.

Correlation Dimensions:

Dimension	Description
Temporal	Alerts within 5-minute window
Topological	Same service or dependency chain
Semantic	Similar alert types or messages
Causal	Upstream → downstream relationships

Example: Correlated Alert Group

{
  "correlation_group": {
    "id": "corr_001",
    "root_cause_alert": "alert_db_001",
    "probable_cause": "Database connection pool exhaustion",
    "confidence": 0.89,
    "correlated_alerts": [
      {
        "alert_id": "alert_api_001",
        "title": "API Gateway 5xx errors spike",
        "correlation_score": 0.95,
        "relationship": "downstream_effect"
      },
      {
        "alert_id": "alert_api_002",
        "title": "API latency increased 500%",
        "correlation_score": 0.92,
        "relationship": "downstream_effect"
      },
      {
        "alert_id": "alert_order_001",
        "title": "Order processing failures",
        "correlation_score": 0.88,
        "relationship": "downstream_effect"
      }
    ],
    "suggested_action": "Increase database connection pool size",
    "runbook_url": "/runbooks/database-connection-pool"
  }
}

Noise Reduction Impact:

Before: 15 separate alerts → After: 1 correlated incident

4. Root Cause Analysis

Automatically identify the most likely root cause using dependency graphs and ML.

RCA Pipeline:

Alert Stream
    ↓
Dependency Graph Analysis
    ↓
Temporal Correlation
    ↓
Semantic Similarity
    ↓
ML Ranking
    ↓
Root Cause Suggestion

Example RCA Output:

{
  "root_cause_analysis": {
    "incident_id": "inc_001",
    "analysis_time_ms": 2340,
    "probable_causes": [
      {
        "rank": 1,
        "cause": "Database connection pool exhaustion",
        "confidence": 0.89,
        "evidence": [
          "Connection pool metrics at 100% for 5 minutes",
          "Query queue length increased 10x",
          "Timeout errors correlate with pool exhaustion"
        ],
        "remediation": "Increase pool size from 100 to 200"
      },
      {
        "rank": 2,
        "cause": "Slow query blocking connections",
        "confidence": 0.65,
        "evidence": [
          "One query running for 45 seconds",
          "Query uses full table scan"
        ],
        "remediation": "Kill long-running query, add index"
      }
    ]
  }
}

5. Auto-Remediation

Automatically execute safe remediation actions for known issues.

Safe Actions (Auto-Approved):

Action	Trigger	Safety Check
Restart service	Repeated crashes	Rate limit: 3/hour
Scale up	CPU/Memory pressure	Budget limit
Clear temp files	Disk >90%	Protected paths
Rotate logs	Log disk full	Retention policy
Refresh cache	Cache errors	Idempotent

Runbook Integration:

{
  "remediation": {
    "alert_id": "alert_001",
    "action": "scale_up",
    "runbook": "/runbooks/auto-scale",
    "status": "executed",
    "result": {
      "success": true,
      "old_replicas": 3,
      "new_replicas": 5,
      "execution_time_ms": 12340
    },
    "safety_checks": [
      {"check": "budget_limit", "passed": true},
      {"check": "rate_limit", "passed": true},
      {"check": "approval_required", "passed": true, "auto_approved": true}
    ]
  }
}

---

Configuration

Enable AIOps

# alerting-config.yaml
aiops:
  enabled: true

  anomaly_detection:
    enabled: true
    baseline_window: 30d
    z_score_threshold: 3.0
    models:
      - isolation_forest
      - prophet

  predictive_alerting:
    enabled: true
    forecast_horizon: 24h
    confidence_threshold: 0.8

  correlation:
    enabled: true
    time_window: 5m
    min_correlation_score: 0.7
    use_dependency_graph: true

  auto_remediation:
    enabled: true
    require_approval:
      - production
    auto_approve:
      - development
      - staging
    rate_limits:
      restart: 3/hour
      scale: 5/hour

Dependency Graph

Define service dependencies for better correlation:

# dependency-graph.yaml
services:
  api-gateway:
    depends_on:
      - auth-service
      - commerce-service
      - platform-service

  commerce-service:
    depends_on:
      - database-primary
      - redis-cache

  database-primary:
    depends_on: []
    type: datastore

Alert Enrichment

Enrich alerts with additional context:

# enrichment-rules.yaml
enrichments:
  - match:
      service: api-gateway
    add:
      team: platform
      runbook_base: /runbooks/api-gateway
      dashboard: https://grafana.olympuscloud.ai/d/api

  - match:
      metric_prefix: database_
    add:
      team: data
      oncall_schedule: sch_data_team

---

API Reference

Get AIOps Insights

GET /api/v1/alerting/aiops/insights

Response:

{
  "insights": [
    {
      "type": "anomaly",
      "severity": "high",
      "metric": "api_latency_p99",
      "description": "99th percentile latency is 3x normal",
      "current_value": 450,
      "baseline": 150,
      "recommended_action": "Check recent deployments"
    },
    {
      "type": "prediction",
      "severity": "medium",
      "metric": "disk_usage",
      "description": "Disk predicted full in 6 hours",
      "current_value": 0.82,
      "predicted_value": 1.0,
      "prediction_time": "2026-01-25T02:00:00Z"
    },
    {
      "type": "correlation",
      "severity": "high",
      "description": "5 alerts appear related",
      "alert_count": 5,
      "probable_cause": "Database latency spike",
      "correlation_id": "corr_001"
    }
  ]
}

Analyze Alert Correlation

POST /api/v1/alerting/aiops/correlate

Request:

{
  "alert_ids": ["alert_001", "alert_002", "alert_003"]
}

Response:

{
  "correlation": {
    "is_correlated": true,
    "correlation_score": 0.91,
    "root_cause_alert": "alert_001",
    "probable_cause": "Memory pressure on commerce-service",
    "dependency_chain": [
      "commerce-service (root)",
      "→ api-gateway (affected)",
      "→ frontend (affected)"
    ]
  }
}

Get Root Cause Analysis

GET /api/v1/alerting/aiops/rca/{incident_id}

Trigger Auto-Remediation

POST /api/v1/alerting/aiops/remediate

Request:

{
  "alert_id": "alert_001",
  "action": "scale_up",
  "parameters": {
    "replicas": 5
  },
  "dry_run": false
}

---

Metrics & Monitoring

AIOps Performance Metrics

Metric	Description	Target
aiops_predictions_accuracy	Prediction accuracy	>85%
aiops_correlations_found	Correlations identified	Maximize
aiops_noise_reduction_ratio	Alert noise reduction	>50%
aiops_rca_accuracy	Root cause accuracy	>80%
aiops_remediation_success_rate	Auto-remediation success	>95%
aiops_analysis_latency_ms	Analysis latency	Under 1000ms

Dashboard

Access the AIOps dashboard at: https://cockpit.olympuscloud.ai/aiops

Dashboard Panels:

• Anomaly detection heatmap
• Prediction accuracy over time
• Correlation graph visualization
• Auto-remediation audit log
• ML model health status

---

Best Practices

For Effective Anomaly Detection

warning

The AIOps engine requires at least 30 days of baseline data before anomaly detection is accurate. Enabling it on a new service before this learning period will result in excessive false positives.

1. Allow baseline learning - Wait 30 days for accurate baselines
2. Tune sensitivity - Adjust z-score threshold per metric
3. Account for seasonality - Enable Prophet for seasonal metrics
4. Exclude maintenance - Use maintenance windows to pause detection

For Better Correlation

1. Maintain dependency graph - Keep service relationships updated
2. Use consistent naming - Standardize alert names and labels
3. Tag alerts properly - Include service, team, environment labels
4. Tune time window - Adjust correlation window for your incident patterns

For Safe Auto-Remediation

danger

Auto-remediation in production requires explicit human approval by default. Never enable auto_approve for the production environment, as automated actions like scaling or restarting services can cascade and cause wider outages if the root cause is misidentified.

1. Start with dry-run - Test remediation actions first
2. Use rate limits - Prevent remediation loops
3. Require approval for prod - Human approval for production
4. Audit everything - Log all automated actions
5. Test runbooks - Verify runbook actions work correctly

---

Troubleshooting

Common Issues

Issue	Cause	Solution
Too many false anomalies	Baseline too short	Wait for 30+ days of data
Predictions inaccurate	Trend change	Retrain with recent data
Correlations missing	Dependency graph outdated	Update service dependencies
Auto-remediation failing	Permission issues	Check service account roles
High latency	Too many alerts	Enable alert sampling

Debug Mode

POST /api/v1/alerting/aiops/debug

Request:

{
  "alert_id": "alert_001",
  "debug_options": {
    "show_baseline": true,
    "show_ml_scores": true,
    "show_correlation_factors": true
  }
}

---

Related Documentation

• Alerting API - Alerting API reference
• Cockpit Guide - Operations center
• Olympus Chat - Incident communication
• Runbooks - Incident response