Multi-signal correlation and root cause analysis (RCA)
This use case explains how to use the Virtana Platform to leverage logs, metrics, and traces to correlate simultaneous infrastructure and application issues. It focuses on how AI-driven analytics groups related alerts into a single incident, suppresses low-value and duplicate noise, and identifies the most probable root cause, including confidence scoring.
You use this workflow when you see symptoms across multiple domains at the same time, such as infrastructure saturation, network degradation, and application errors, and you need a single place to understand what is really happening.
Scenario
In this use case, multiple performance and reliability issues occur at the same time in a production environment, including:
CPU spikes on a critical application host
Network latency and packet drops between services
A burst of application-level errors
Increased response times reported by end users
The environment runs a microservices-based application that produces distributed traces, structured application logs, and infrastructure and container metrics. All these signals are ingested and analyzed by the Virtana platform to provide comprehensive visibility and insights.
Setup
The environment is a monitored Kubernetes-based microservices platform with logging, tracing, topology mapping, and AI analytics enabled. Simulated issues produce telemetry data. The monitoring system detects simultaneous anomalies, which trigger multiple alerts.
Environment configuration
The environment is configured as a Kubernetes-based microservices platform with the following features enabled or configured:
Application Performance Monitoring
Log aggregation and indexing
Distributed tracing instrumentation
Infrastructure and network monitoring
Service topology and dependency
AI/Analytics engine
Trigger
When the real issues occur, the monitoring system detects concurrent anomalies, such as:
Host CPU utilization exceeding 90%
Network latency values breaching the configured thresholds
Application error rate spikes
Increased trace duration
These events generate multiple alerts across different domains.
Use case workflow
This use case performs the following steps.
Step 1: Alert correlation
You start by opening the Alerts page in Virtana Global View. The system identifies a critical alert, Billing Service Performance Degradation, and automatically links related alerts based on custom policies. Redundant and low‑impact alerts are suppressed to reduce noise.
At this stage, Virtana Platform has already grouped many raw alerts from different monitoring sources into a single incident that represents the end-user impact and the affected services.
Step 2: Influence metrics and topology in the context of the alert
After selecting the active incident, open the correlation panel. The platform displays a unified timeline of metrics, logs, and traces, along with service dependency context. For example, you can view the trace that reveals a slow database call, the logs indicate timeout errors, and the metrics show CPU saturation. All telemetry signals are synchronized on a single timeline to highlight their relationships.
Step 3: Navigation across telemetry signals
As a user, you can drill into a specific affected transaction to investigate further. Review the distributed trace, switch to the corresponding log entries, and examine linked infrastructure metrics. This workflow provides comprehensive end‑to‑end visibility for the same request.
Step 4: Root cause identification
The AI analytics engine evaluates dependency relationships alongside correlated metrics to determine the most probable root cause of the incident.
Observations
Virtana Platform correlates multi-signal telemetry into actionable insights, highlighting key patterns, behaviors, and outcomes observed during incident analysis.
Depth of correlation
The platform correlates logs, traces, and metrics with full cross‑domain dependency awareness, ensuring all telemetry is synchronized in time for accurate analysis.
Noise reduction and alert quality
Alerts are deduplicated, context‑aware suppression is applied, and correlation policies are used to minimize unnecessary noise and highlight the most relevant events.
Global View alerts
The alert identifies the affected service and its environment, and relies on service state, recent activity, and related entities instead of direct performance metrics. An automated analysis update in the activity log confirms that the recurring failure has been recognized, and the alert view provides quick navigation to related application and infrastructure context so you can assess how this ongoing service outage impacts the rest of the application.
Benefits
When you use multi-signal correlation and AI-driven root cause analysis in Virtana Platform, you have the following benefits:
Faster root cause identification
Reduced alert fatigue
Improved cross-team collaboration
Higher service reliability
Summary
This use case illustrates how the Virtana platform correlates logs, metrics, and traces to transform fragmented alerts into actionable intelligence. By combining AI-driven analysis, transparent reasoning, and impact-aware alert management, teams canrapidly isolate root causes and restore service performance.