Introduction
Automated observability uses ML to detect anomalies and trigger auto-remediation, reducing MTTR from hours to seconds.
Key Statistics:
- Auto-remediation reduces MTTR by 80%
- ML anomaly detection: 90% accuracy
- 40% of incidents can be auto-remediated
Architecture
┌─────────────────────────────────────────────────────────────────┐
│ Automated Observability Flow │
├─────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ Metrics │───▶│ Anomaly │───▶│ Decision │ │
│ │ Logs │ │ Detection │ │ Engine │ │
│ │ Traces │ │ (ML Model) │ │ │ │
│ └─────────┘ └─────────────┘ └──────┬──────┘ │
│ │ │
│ ┌───────────────────────┬┘ │
│ ▼ ▼ │
│ ┌─────────────┐ ┌─────────────┐ │
│ │ Automatic │ │ Manual │ │
│ │ Remediation │ │ Alert │ │
│ └─────────────┘ └─────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘
Anomaly Detection
#!/usr/bin/env python3
"""Anomaly detection for metrics."""
import numpy as np
from sklearn.ensemble import IsolationForest
class AnomalyDetector:
"""Detect anomalies in time-series metrics."""
def __init__(self, contamination=0.1):
self.model = IsolationForest(
contamination=contamination,
random_state=42
)
self.baseline = None
def train(self, historical_data):
"""Train on historical data."""
features = self.extract_features(historical_data)
self.model.fit(features)
self.baseline = {
'mean': np.mean(historical_data),
'std': np.std(historical_data)
}
def detect(self, current_value):
"""Detect if current value is anomalous."""
# Z-score based detection
z_score = (current_value - self.baseline['mean']) / self.baseline['std']
is_anomaly = abs(z_score) > 3
return {
'anomaly': is_anomaly,
'z_score': z_score,
'severity': 'high' if abs(z_score) > 4 else 'medium' if abs(z_score) > 3 else 'low'
}
def extract_features(self, data):
"""Extract features for ML model."""
features = []
for i in range(len(data) - 10):
window = data[i:i+10]
features.append([
np.mean(window),
np.std(window),
np.min(window),
np.max(window),
window[-1] - window[0] # Trend
])
return np.array(features)
Auto-Remediation
#!/usr/bin/env python3
"""Automated remediation actions."""
class AutoRemediator:
"""Execute automated remediation."""
def __init__(self, k8s_client, cloud_client):
self.k8s = k8s_client
self.cloud = cloud_client
def handle_high_cpu(self, pod):
"""Auto-scale or restart high CPU pod."""
# Check if auto-scaling is enabled
if self.is_hpa_enabled(pod):
# Let HPA handle it
return {"action": "hpa_will_handle"}
# Restart pod if not HPA
self.k8s.delete_pod(pod)
return {"action": "pod_restarted", "pod": pod}
def handle_high_memory(self, pod):
"""Restart pod with memory issue."""
self.k8s.delete_pod(pod)
return {"action": "pod_restarted", "reasonoom"}
def handle_database_connection(self, issue": "memory_):
"""Handle database connection pool exhaustion."""
# Scale up database connections
self.cloud.scale_database(
instance=issue['database'],
new_size=issue['current_size'] * 2
)
return {"action": "database_scaled", "new_size": issue['current_size'] * 2}
def handle_redis_full(self):
"""Clear Redis cache."""
# Evict oldest keys
self.redis.execute('EVICT', 1000)
return {"action": "cache_evicted", "keys": 1000}
Integration
# Automated response rules
automations:
- name: "High CPU Remediation"
trigger:
metric: "container_cpu_usage_seconds_total"
condition: "> 0.9 for 5m"
actions:
- type: "scale"
target: "deployment"
action: "scale_up"
max_replicas: 20
- type: "notify"
channel: "#ops"
- name: "Pod Crash Loop"
trigger:
metric: "kube_pod_container_status_restarts"
condition: "> 5 in 10m"
actions:
- type: "diagnose"
gather_logs: true
- type: "rollback"
last_known_good: true
- name: "Database High Connections"
trigger:
metric: "pg_stat_activity_count"
condition: "> 0.8 * max_connections for 2m"
actions:
- type: "scale"
target: "database"
- type: "notify"
channel: "#database-alerts"
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