NordIQ Dashboard: Predictive Monitoring

8-Hour Prediction Horizon

Our Temporal Fusion Transformer (TFT) model analyzes 24 hours of historical data to predict server behavior 8 hours into the future. You get 30-60 minute advance warning before incidents become critical.

• Predictions refreshed every 5 seconds
• 88% accuracy on critical incidents
• GPU-accelerated inference (<100ms per server)
• Real-time WebSocket streaming to dashboard

Contextual Risk Intelligence

We don't just alert on raw thresholds. Our fuzzy logic system understands operational context—what's normal for your infrastructure, what's trending dangerous, and what requires immediate action.

• Profile Awareness: Database at 98% memory = healthy (page cache). ML server at 98% = critical (OOM imminent).
• Trend Analysis: 40% CPU steady = fine. 40% CPU climbing from 20% = dangerous trend detected.
• Multi-Metric Correlation: High CPU alone = watch. High CPU + high memory + high I/O wait = critical compound stress.
• Prediction-Aware: Current 40%, predicted 95% = early warning. Current 85%, predicted 60% = resolving issue.

Result: Intelligent alerts that understand your environment, not just arbitrary thresholds.

7 Graduated Severity Levels

Traditional monitoring: everything is either OK or ON FIRE. Our system: graduated escalation with appropriate response times.

• 🔴 Imminent Failure (90+): 5-minute SLA, CTO escalation, emergency response
• 🔴 Critical (80-89): 15-minute SLA, page on-call engineer
• 🟠 Danger (70-79): 30-minute SLA, team lead notification
• 🟡 Warning (60-69): 1-hour SLA, team awareness
• 🟢 Degrading (50-59): 2-hour SLA, email notification
• 👁️ Watch (30-49): Background monitoring, no alerts
• ✅ Healthy (0-29): Normal operation

Benefit: Right-sized responses. No alert fatigue. No false positives.

Profile-Based Transfer Learning

New servers get accurate predictions immediately—no training period required. Our model learns patterns from similar servers and applies that intelligence to new infrastructure.

7 Server Profiles:

• ML Compute: Training nodes, high CPU/memory bursts
• Database: Oracle/Postgres, high disk I/O, large page cache
• Web API: REST endpoints, high network throughput
• Conductor/Management: Job scheduling, orchestration
• Data Ingest: Kafka/Spark streaming, high write volume
• Risk Analytics: Financial calculations, CPU-intensive
• Generic: Fallback for unknown workloads

Benefits:

• ✅ No retraining when adding servers of known types
• ✅ 13% better accuracy than generic models
• ✅ 80% less retraining frequency
• ✅ Immediate production value for new infrastructure

14 Production Metrics

We monitor the metrics that matter for real-world troubleshooting—not just CPU and memory.

• CPU: User, System/Kernel, I/O Wait, Idle, Java/Spark-specific
• Memory & Storage: Utilization %, Swap usage, Disk space
• Network & System: Ingress/Egress (MB/s), TCP connections, Load average, Uptime

Why I/O Wait matters: High I/O wait is "system troubleshooting 101"—the first metric experienced engineers check when diagnosing performance issues.

Real-Time Streaming Architecture

Microservices-based design for high performance and scalability.

• Inference Daemon: REST API + WebSocket streaming
• Metrics Generator: Collects data from production sources
• Dashboard: Interactive web UI with 10 specialized tabs
• Performance: <100ms inference latency, <2s dashboard load time
• Caching: Strategic caching provides 60% performance improvement