Improved trust in shared GPU infrastructure by making usage, anomalies, and thresholds easier to understand.
GPU Monitoring & Alerting System
Building real-time GPU visibility and alerting for cloud users
Built a metrics and alerting system that turned raw GPU usage data into operational visibility for users and operators.
Contributed across ingestion design, Spring Boot threshold processing, InfluxDB storage, and dashboard visibility.
Cloud Monitoring System
Why this matters
Real-time GPU metrics collection and alerting
Spring Boot + InfluxDB pipeline for time-series monitoring
Dashboard visibility for users and operators
Visual Evidence
What to show hereMetrics to alerting pipeline
The most persuasive visual for this work is the path from raw GPU metrics through storage, threshold evaluation, and dashboard feedback.
Replace this panel with a screenshot, diagram, or product crop when you have one. For now it acts as a guided visual placeholder.
Replace this panel with a screenshot, diagram, or product crop when you have one. For now it acts as a guided visual placeholder.
Replace this panel with a screenshot, diagram, or product crop when you have one. For now it acts as a guided visual placeholder.
1. Context
- Built for a cloud environment where users and operators depended on shared GPU resources.
- The project improved trust in infrastructure usage visibility and operational response.
2. Problem
Users and operators lacked real-time visibility into GPU resource usage. Without proper monitoring and alerting, it was difficult to detect abnormal usage, performance issues, or resource availability problems.
3. Constraints
- Needed near real-time visibility for operational usefulness.
- Required a time-series storage layer for metrics.
- Threshold-based alerting had to be understandable and controllable.
- Dashboard views needed to be legible for both users and operators.
- Alert noise had to be reduced so people would trust the signals.
4. Solution
- Implemented GPU metric collection and ingestion paths.
- Stored time-series data in InfluxDB for fast trend queries.
- Processed thresholds in Spring Boot and generated alerts.
- Built dashboard displays in React to show status and graphs clearly.
- Focused on practical visibility rather than overcomplicated controls in the MVP.
5. Architecture / Flow
GPU metrics collected
This is the trigger or incoming operational event.
Metrics stored in InfluxDB
This stage moves the workflow toward a reliable production result.
Spring Boot API processes metric data
This stage moves the workflow toward a reliable production result.
Threshold rules evaluated
This stage moves the workflow toward a reliable production result.
Alert generated if needed
This stage moves the workflow toward a reliable production result.
React dashboard displays status and graphs
This is the observable outcome for operators or users.
6. Trade-offs
| Option | Pros | Cons | Decision |
|---|---|---|---|
| Polling-based collection | Simpler to implement and operate | Less real-time than streaming | Used polling for the initial implementation |
| Streaming-based collection | Higher immediacy | Increased complexity | Kept as a future improvement path |
| Alert on every event | Less chance of missing anomalies | Creates alert fatigue | Used threshold-based alerting to balance signal quality |
| Short retention | Lower storage cost | Weaker historical analysis | Adjusted retention to match operational needs |