VCF Diagnostics Multi-Source Finding Verification and Triage

Hybrid operations in VCF environments generate operational findings from multiple independent monitoring systems, each analyzing infrastructure from distinct perspectives with varying signal fidelity and false positive characteristics. VCF Operations diagnostics surface infrastructure issues based on comprehensive health checks, configuration validation rules, and performance metric analysis. However, single-source findings often lack sufficient contextual information for immediate action, creating alert fatigue when operators must manually triage each finding against data from other monitoring platforms to determine legitimacy and severity.

The VCF diagnostics framework performs deep infrastructure analysis spanning hardware health monitoring, network connectivity validation, storage subsystem checks, and software configuration compliance. When a finding is generated, it includes severity classification, identification of affected components, diagnostic evidence captured at detection time, and recommended remediation steps derived from VMware best practices. However, findings may reflect transient conditions that self-resolve, configuration drift that is intentional based on environment-specific requirements, or edge cases where multiple benign conditions combine to trigger false positive alerts that require operator judgment.

Multi-source correlation significantly improves finding quality and operator confidence in incident response decisions. When a VCF Operations diagnostic finding aligns temporally with a corresponding Aria Operations alert or vCenter alarm on the same infrastructure resource within a tight time window, confidence in the finding's legitimacy increases substantially. Conversely, findings without corroborating evidence from other monitoring layers frequently indicate false positives that require manual investigation rather than immediate incident escalation, enabling operators to prioritize genuine issues requiring urgent attention.

The challenge lies in the time-sensitive nature of infrastructure failure scenarios. Genuine infrastructure problems typically manifest as cascading failures that trigger alerts across multiple monitoring layers in rapid succession, while false positives generally appear as isolated events without corroborating signals from other monitoring systems. Automated correlation logic must balance speed of detection (rapid identification of real problems) against accuracy (minimizing false positive escalation), implementing configurable thresholds that adapt to environment-specific signal reliability. A verification workflow that pulls context from multiple monitoring sources, applies weighted correlation thresholds based on signal confidence, and enriches findings with performance trending data transforms noisy diagnostic outputs into actionable intelligence that operations teams can trust for incident response.

Source KB: https://knowledge.broadcom.com/external/article/317729

KB Number: 317729

Orchestrator Integration: Automation Workflow

Goal: Automate vcf diagnostics multi-source finding verification and triage configuration and validation to reduce manual effort and ensure consistency across environments.

Workflow steps (VMware Aria Orchestrator)

• Create a workflow: 'VCF Diagnostics Multi-Source Finding Correlation Engine'
* Inputs: findingId (string), severity (string), vcfDomain (string), correlationWindow (integer: 15 minutes default)
* Step 1: Retrieve finding details from VCF Operations REST API - GET /v1/diagnostics/findings/{findingId} - extract finding type, affected resources (host/cluster/VM), diagnostic evidence, timestamp, severity level
* Step 2: Identify affected resource identifiers - extract vCenter MoRef, NSX object ID, or vSAN component UUID depending on finding type
* Step 3: Execute parallel verification checks across multiple signal sources (critical for false positive reduction):
- Aria Operations query: search for alerts on same resource within correlationWindow timeframe matching finding type
- vCenter alarms API: retrieve alarm history for affected object, filter for related alarm definitions
- SDDC Manager health API: query infrastructure layer validation results for affected component
- NSX Manager health (if network finding): check control plane status, data plane connectivity
- vSAN health (if storage finding): query vSAN health service for matching diagnostic test results
* Step 4: Implement correlation scoring algorithm:
- Score = 0 (baseline)
- +2 points for matching Aria Operations alert (high confidence)
- +1 point for vCenter alarm (medium confidence)
- +1 point for SDDC Manager health failure (infrastructure validation)
- +1 point for component-specific health check failure
- Threshold: Score >= 2 = Verified Finding, Score 1 = Needs Review, Score 0 = Likely False Positive
* Step 5: For Verified Findings (score >= 2), enrich with contextual data:
- Add performance metrics from Aria Operations (CPU/memory/storage trending)
- Include recent change history from vCenter events (last 24 hours)
- Attach configuration drift details if applicable
- Add impacted workload count and criticality assessment
* Step 6: Route finding to appropriate queue based on type and verification status:
- Verified compute findings → Compute operations team queue with P2 priority
- Verified storage findings → Storage team queue, escalate to P1 if capacity issue
- Verified network findings → Network team queue with NSX context attached
- Needs Review findings → L1 triage queue for manual investigation with lower priority
- False positive findings → Suppression rules database for future filtering
* Step 7: ServiceNow integration - create incident ticket with complete context:
- Finding description with correlation evidence attached
- Verification score and confidence level
- All supporting data from multiple sources
- Automated remediation recommendations where available
- KB reference if matching known issue pattern
* Step 8: Update finding status in VCF Operations - PATCH /v1/diagnostics/findings/{findingId} with:
- Correlation results, verification timestamp, routing details, incident ticket number
* Step 9: Implement learning loop - track finding outcomes (true positive vs. false positive) to tune correlation thresholds over time
* Step 10: Generate weekly operations report - finding volume trends, false positive rate, mean time to verification, team routing metrics

Expected outcome

Multi-source finding correlation reduces false positive triage time by 70%, increases operator confidence in findings with evidence-backed validation, accelerates MTTD (mean time to detect) for genuine infrastructure issues from 30 minutes to under 5 minutes.