Data Center Ops: Telemetry & Observability
Telemetry and observability provide the raw data streams and insights that keep data centers running. For hyperscale and AI-native facilities, millions of signals flow from servers, racks, power systems, cooling, and networks. Modern observability platforms transform this data into actionable intelligence for monitoring, troubleshooting, and optimization.
Core Telemetry Sources
| Domain | Examples | Purpose |
|---|---|---|
| IT Systems | Server logs, GPU metrics, hypervisor events | Monitor workload health and performance |
| Networking | Flow logs, packet captures, switch/router stats | Detect congestion, anomalies, or intrusions |
| Power | EPMS data: voltage, harmonics, breaker status | Ensure stable, reliable electricity supply |
| Cooling | BMS data: CRAC/CRAH, liquid loops, temperature sensors | Prevent hotspots and maintain thermal balance |
| Facility | DCIM asset tracking, environmental sensors | Capacity planning and space optimization |
| Security | Access logs, CCTV feeds, IDS/IPS events | Correlate cyber + physical incidents |
Observability Layers
- Metrics: Numeric data (CPU utilization, PUE, network throughput).
- Logs: Discrete events from servers, apps, and devices.
- Traces: Distributed traces across microservices and workloads.
- Events: Alerts and anomalies detected from telemetry streams.
Benefits
- Early Detection: Spot anomalies before they cause outages.
- Root-Cause Analysis: Link symptoms across IT, power, and cooling.
- Optimization: Identify inefficiencies and improve PUE, WUE, CUE.
- Automation: Feed AIOps and orchestration platforms with clean signals.
Challenges
- Volume: Exascale clusters generate billions of telemetry points daily.
- Data Silos: IT, OT, and facility data often separated.
- Signal-to-Noise: Too many alerts without correlation leads to operator fatigue.
- Retention: Long-term telemetry storage is costly but needed for compliance.
Key Technologies & Platforms
| Vendor/Platform | Focus | Notes |
|---|---|---|
| Prometheus / Grafana | Open-source metrics and visualization | Widely used for IT observability |
| Splunk | Log aggregation and search | Strong integration with AIOps |
| Elastic Stack (ELK) | Logs, metrics, traces | Flexible open-source stack for observability |
| Datadog | Cloud observability platform | Unified metrics, logs, traces at scale |
| Hyperscaler Tools | Google Stackdriver, AWS CloudWatch, Azure Monitor | Integrated into cloud-native ops |
| Facility Vendors | DCIM/BMS/EPMS dashboards | Native telemetry for OT/energy systems |
Emerging Trends
- Streaming Observability: Real-time pipelines (Kafka, Pulsar) for exascale telemetry.
- AI Correlation: ML used to reduce noise and surface actionable insights.
- Digital Twins: Feeding observability data into simulation models for planning.
- Cross-Domain Analytics: Correlating IT, OT, and energy data for holistic insight.