Storage / Data-Path Degradation Risk
Detect deterioration in the storage path before file-service impact becomes visible to customers.
Unacknowledged
Risk score
82/ 100Critical
+18 vs. 24h ago
Status
Critical risk
Confidence: 87%
Estimated lead time
~2–6 hours
Based on current trend
Risk score trend
Evidence
Data disk latencyazure
p95 data-disk latency 3.5× baseline on 4 clusters in East US 2 / zone 1
Disk throttlingazure
IOPS consumed at 96% on 14 data disks; burst credits 88% used
Qumulo IO latencyqumulo
Read latency +180% on 4 clusters
How this risk score works
- What it is
- A 0–100 estimate of how likely Storage / Data-Path are to cause customer impact soon. Higher means more signals are moving away from normal at the same time.
- How it is calculated
- Each signal is compared with its own baseline (same hour, last 14 days). The further it is above baseline, the more points it adds, up to a fixed weight per signal. The points add up to the score, capped at 100. Method: baseline deviation + multi-signal correlation.
- This score
- Data Disk Latency (p95) +16, Data Disk IOPS Consumed +13, Data Disk Bandwidth Consumed +9, other signals +44 = 82 (Critical), +18 vs. 24h ago.
- 0–25 Normal26–50 Elevated51–75 High76–100 Critical
- What it tells you
- Impact is likely in ~2–6 hours if the trend continues. Start with Data Disk Latency (p95), which adds the most points. Confidence 87% reflects how many expected data feeds reported.
Contributing signals
Score breakdown82 / 100
Select a signal to see the clusters, regions and disks behind it.
| Signal | Current | Baseline | Points | Impact |
|---|---|---|---|---|
| 42 ms | 12 ms | +16 | high | |
| 96% | 55% | +13 | high | |
| 91% | 50% | +9 | high | |
| 38 | 4 | +9 | medium | |
| 88% | 20% | +5 | medium | |
| 82% | 15% | +3 | medium | |
| 84% | 45% | +5 | medium | |
| 79% | 40% | +3 | medium | |
| 2.1% | 0.1% | +5 | medium | |
| 28 ms | 10 ms | +5 | medium | |
| 3,900 | 2,400 | +2 | low | |
| 2,900 | 1,700 | +2 | low | |
| 1.1 GB/s | 0.7 GB/s | +1 | low | |
| 0.8 GB/s | 0.5 GB/s | +2 | low | |
| 9 ms | 4 ms | +2 | low |
Affected resources (6)
| Resource | Type | Region / AZ | Current Risk |
|---|---|---|---|
| rg-anq-prod-eus2-c0142 | Qumulo Cluster | East US 2 / 1 | 86 |
| disk-anq-c0142-data-07 | Data Disk | East US 2 / 1 | 82 |
| disk-anq-c0142-data-12 | Data Disk | East US 2 / 1 | 76 |
| rg-anq-prod-eus2-c0217 | Qumulo Cluster | East US 2 / 1 | 72 |
| stanq0142data01 | Storage Account | East US 2 / — | 68 |
| rg-anq-prod-eus2-c0388 | Qumulo Cluster | East US 2 / 2 | 64 |
Affected clusters (3)
| Cluster | Resource group | Current Risk |
|---|---|---|
| ANQ-0142 · Media & Entertainment | rg-anq-prod-eus2-c0142 | 86 |
| ANQ-0217 · Life Sciences | rg-anq-prod-eus2-c0217 | 72 |
| ANQ-0388 · Retail & Distribution | rg-anq-prod-eus2-c0388 | 64 |
Recommended actions
- 1Open Data Disk Latency to find the clusters and disks contributing most.
- 2Check for concurrent Azure platform events in East US 2.
- 3Validate customer impact and communicate if the trend continues.