Planning for the future: Capacity forecasting and resource optimization

In this use case, your environment typically includes:

Virtana Platform ingests data from multiple domains:

You can open the Global Capacity dashboard in Virtana Platform. This dashboard typically shows total storage capacity for SAN and NAS pools, overall cloud and on-premises utilization, and AI/LLM workload usage trends such as GPU hours and token volume. It also displays compute and memory growth over time, along with power and energy consumption metrics.

For example, you can view the indicator, Primary Storage Pool: 87% used – 42 days to projected exhaustion. From this single view, you can quickly identify which domains or resource pools are approaching capacity limits.

You can navigate to the Cloud Cost Management dashboard. In this view, you examine cost breakdowns by business unit, by application or service, and by environment, such as dev, test, and production. The dashboard highlights underutilized or idle cloud instances, unused or low-utilization storage volumes, and the mix of reserved, savings plan, and on-demand usage.

For example, you might discover that the Marketing business unit accounts for 28% of cloud spend, with approximately 40% of resources identified as idle or underutilized. You can connect cloud spending to actual usage, and then identify optimization opportunities, such as right-sizing instances, removing unused resources, or moving workloads to more cost-effective tiers.

You then drill into Kubernetes capacity and efficiency views. You can review a few key metrics, such as CPU and memory requests versus limits at the namespace, deployment, and pod levels, as well as node overcommitment and consolidation potential. You also look at signals such as pending pods, scheduling pressure, and cluster saturation risk indicators.

You can discover insights, for example, the Payments cluster is overcommitted by 35% CPU due to inflated pod requests, causing inefficient node utilization and higher cloud costs. Based on this analysis, you can identify the clusters that could be consolidated or right-sized and areas where autoscaling policies may need adjustment.

Virtana Platform applies predictive models to historical usage data to forecast when resources are likely to be exhausted.

Example forecast:

Based on such forecasts, the platform generates proactive alerts or planning signals.

You can view the workload right-sizing to see optimization opportunities across VMs and Kubernetes workloads.

Common recommendations:

Automated scripts perform the recurring optimization actions.

For your virtual machines (VMs), the VM Coordinator analyzes imbalance across clusters and hosts, and recommends rebalancing actions. It might recommend moving VMs away from overloaded hosts to reduce contention, rebalancing memory-intensive workloads across the cluster, or temporarily relaxing non-critical affinity rules to improve balance.

For example, it might recommend that you "Migrate VM-342 and VM-355 to Host-07 to reduce CPU utilization hotspots and balance load across the cluster.” You can then evaluate the impact, apply changes, and monitor improvements in host utilization and performance.

The analytics engine also evaluates seasonal and cyclical patterns in your environment. For example, it might detect that, “Unusual write activity detected on Storage Array X compared to the six-week seasonal baseline.”

By highlighting deviations from normal patterns, the platform helps you to identify abnormal growth early and can differentiate between expected seasonal peaks and true anomalies.

You can assess the reliability and stability of exhaustion predictions, understand how sensitive they are to trend changes and outliers, and compare seasonality-aware forecasts with simpler linear growth models.

Over time, you can refine retention windows, baselines, and modeling parameters to improve forecast quality.

You gain clear visibility into the correlation between spend and usage, where waste, idle resources, or over-provisioning occur, and how costs are distributed across business units, projects, or services.

This level of alignment supports show back/chargeback models and more accountable capacity planning.

Across the environment, you can identify specific resource optimization opportunities, including Kubernetes and VM efficiency gaps, storage and compute imbalances, and AI workload density and scheduling inefficiencies.

These insights form a prioritized backlog of optimization actions.

Virtana Platform supports proactive risk management by providing early-warning alerts tied to forecasted exhaustion, enabling capacity-driven planning discussions with stakeholders, and reducing the need for emergency purchases and last-minute migrations.