Datacenter Monitoring Platform

Enterprise-grade monitoring platform providing complete visibility into datacenter infrastructure, from power and cooling to compute and network resources.

Overview

A unified monitoring solution that aggregates metrics from thousands of devices, providing real-time insights and alerting for datacenter operations.

Features

• Multi-Layer Monitoring: Infrastructure, compute, network, and application metrics
• Custom Exporters: Purpose-built exporters for datacenter equipment
• Real-Time Dashboards: Grafana dashboards for all infrastructure layers
• Intelligent Alerting: Context-aware alerts with escalation policies
• Capacity Planning: Historical data analysis and trend forecasting
• SLA Reporting: Automated uptime and performance reporting

Architecture

Data Sources → Exporters → Prometheus → Grafana
                              ↓
                        Alertmanager → PagerDuty/Slack
                              ↓
                          Long-term Storage (Thanos)

Monitored Systems

Infrastructure

• PDUs: Power consumption, voltage, current per outlet
• UPS: Battery status, load, runtime
• CRAC/CRAH: Temperature, humidity, airflow
• Environmental: Temperature sensors, water detection

Compute

• Servers: CPU, memory, disk, network utilization
• GPUs: NVIDIA DCGM metrics, temperature, power
• Storage: RAID status, disk health, IOPS
• Hypervisors: VM metrics, resource allocation

Network

• Switches: Port utilization, errors, drops
• Routers: BGP status, routing tables
• Firewalls: Connection counts, throughput
• Load Balancers: Backend health, request rates

Custom Exporters

PDU Exporter

# Monitor APC PDU via SNMP
class PDUExporter:
    def collect(self):
        # Per-outlet power consumption
        for outlet in range(1, 25):
            power = snmp_get(f'outlet.{outlet}.power')
            yield Gauge('pdu_outlet_power_watts', power, 
                       labels={'outlet': outlet})
        
        # Total PDU load
        total_load = snmp_get('pdu.total.load')
        yield Gauge('pdu_total_load_amps', total_load)

IPMI Exporter

# Monitor server hardware via IPMI
class IPMIExporter:
    def collect(self):
        # Temperature sensors
        temps = ipmitool.get_sensor_data('temperature')
        for sensor, value in temps.items():
            yield Gauge('ipmi_temperature_celsius', value,
                       labels={'sensor': sensor})
        
        # Fan speeds
        fans = ipmitool.get_sensor_data('fan')
        for fan, rpm in fans.items():
            yield Gauge('ipmi_fan_rpm', rpm,
                       labels={'fan': fan})

Dashboards

Datacenter Overview

• Total power consumption
• PUE (Power Usage Effectiveness)
• Cooling efficiency
• Rack-level heat maps
• Capacity utilization

Compute Dashboard

• CPU/Memory utilization across fleet
• GPU utilization and temperature
• Storage capacity and IOPS
• Top resource consumers

Network Dashboard

• Bandwidth utilization
• Packet loss and errors
• BGP peer status
• Top talkers

Alerting Rules

groups:
  - name: datacenter
    rules:
      # High temperature alert
      - alert: HighDatacenterTemp
        expr: datacenter_temperature_celsius > 27
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "High temperature in {{ $labels.location }}"
      
      # Power capacity alert
      - alert: PDUCapacityHigh
        expr: (pdu_total_load_amps / pdu_capacity_amps) > 0.8
        for: 10m
        labels:
          severity: critical
        annotations:
          summary: "PDU {{ $labels.pdu }} at {{ $value }}% capacity"
      
      # GPU temperature
      - alert: GPUOverheating
        expr: dcgm_gpu_temp > 85
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "GPU {{ $labels.gpu }} temperature {{ $value }}°C"

Tech Stack

• Prometheus: Metrics collection and storage
• Grafana: Visualization and dashboards
• Alertmanager: Alert routing and deduplication
• Thanos: Long-term metrics storage
• Python: Custom exporters
• SNMP: Device monitoring
• IPMI: Server hardware monitoring

Deployment

# Deploy monitoring stack
docker-compose up -d

# Add new exporter
./add-exporter.sh --type pdu --target 10.1.1.100

# Import dashboards
./import-dashboards.sh

# Configure alerts
./configure-alerts.sh --pagerduty-key $KEY

Metrics Collected

• 15,000+ unique time series
• 1 million+ samples per minute
• 90 days high-resolution retention
• 2 years downsampled retention

Impact

• MTTR Reduction: 60% faster incident response
• Proactive Alerts: 80% of issues detected before user impact
• Capacity Planning: 6-month accurate forecasting
• Cost Savings: $200K/year in prevented downtime

Integration

• PagerDuty for on-call alerting
• Slack for team notifications
• ServiceNow for ticket creation
• Elasticsearch for log correlation