Virtana Platform workflows

Virtana’s observability platform provides comprehensive visibility across the entire stack, from infrastructure (compute, storage, network) through containers and hybrid environments, up to services, applications, and AI workloads.

This use case explains how Virtana builds a unified telemetry fabric that correlates signals across layers instead of isolated silos. Uses Global View as a centralized observability layer to ingest, correlate, and visualize signals from on‑prem, cloud, and hybrid environments. To view in detail, see Full-stack view.

This use case shows how Virtana Platform correlates logs, metrics, and traces to turn fragmented alerts into a single, explainable incident view with a clear probable root cause.

You can follow a scenario where multiple issues occur simultaneously, such as CPU spikes, network latency, error bursts, and slow end‑user response times. Virtana ingests distributed traces, structured logs, and infrastructure/container metrics. You can also review the Global View groups related alerts by service topology, suppress duplicates, and highlight a single critical incident. To view in detail, see Multi-signal correlation and root cause analysis.

This use case demonstrates how Virtana Platform connects business KPIs with operational telemetry using highly customizable dashboards.

Virtana integrates business and financial data such as cloud spend, revenue, and transaction volume with custom metrics from scripts, sensors, and SSL checks, as well as operational telemetry across infrastructure, containers, applications, traces, and SLAs.

You can define key KPIs and thresholds that capture revenue, cost, customer experience, and reliability targets, and then assemble dashboards that surface these indicators in a concise, business‑friendly view. To view in detail, see Use Case: Business KPI customizable dashboards.

This use case illustrates how Virtana uses policy‑based automation and integrations to build self‑healing systems with strong governance and auditing. It begins with a recurring failure, such as CPU saturation on production billing hosts, which is captured as a high‑severity alert and associated with an automation policy in the Alert Responses.

When the alert fires, Virtana automatically executes a multi‑step remediation process that can notify stakeholders, open or update tickets, run diagnostics, restart services or VMs, and verify that metrics return to healthy levels. To view in detail, see Self-healing system.

This use case describes an end‑to‑end workflow for investigating and resolving a slow, revenue-critical application experiencing an SLA breach. AI‑driven recommendations highlight likely problem areas, such as elevated storage latency on a specific database volume. To view in detail, see Debugging a slow application.

This use case shows how Virtana helps organizations shift from reactive firefighting to proactive capacity and cost planning across cloud, Kubernetes, virtualization, storage, and AI/LLM workloads. Virtana recommends workload right‑sizing for virtual machines and Kubernetes workloads, as well as consolidation or scale‑down opportunities for idle AI clusters.

VM Coordinator helps rebalance clusters by suggesting VM moves across hosts, and seasonal trend detection distinguishes expected growth patterns from anomalous spikes. Together, these capabilities support predictable infrastructure growth, lower cloud and AI costs, and better overall utilization. To view in detail, see Planning for the future.

This use case explains how Virtana supports engineering and operations teams in validating application releases using data‑driven comparisons across versions and time windows. By reviewing these differences, teams can evaluate whether the new release introduces performance or reliability risks, decide whether to proceed or roll back, and communicate findings to developers with concrete evidence. This approach reduces failed releases and rollbacks, shortens feedback cycles, and underpins confident go/no‑go decisions. To view in detail, see Release validation.

This use case focuses on how Virtana Cloud Cost Management (CCM) helps you to understand and control rising cloud spend. The Cost Saving Opportunities view identifies idle or underutilized resources and presents concrete right‑sizing recommendations that quantify potential savings.

To prioritize actions, you can rely on Cost vs Utilization (CvU) reports that correlate instance‑level spend with actual utilization, making it easy to decide which resources should be resized, scheduled, or terminated. You can compile these insights into business‑oriented summaries that show long‑term trends, top cost drivers, and the savings that can be achieved by implementing Virtana’s recommendations, putting continuous cost governance on a repeatable footing. To view in detail, see Use Case: Detecting, Diagnosing, and Optimizing Rising Cloud Costs.