Capacity Optimization in vRealize Operations Manager is achieved using powerful integrated functions - capacity overview, workload balancing and optimization, repurposing of underutilized resources, and what-if predictive scenarios - to reach optimal system performance.
Capacity planners must assess whether physical capacity is sufficient to meet current or forecasted demand. With robust capacity planning and optimization, you can manage your production capacity effectively as your organization addresses changing requirements. The objective of strategic capacity optimization is to reach an optimal level where production capabilities meet ongoing demand.
vRealize Operations Manager analytics provide precise tracking, measuring and forecasting of data center capacity, usage, and trends to help manage and optimize resource use, system tuning, and cost recovery. The system monitors stress thresholds and alerts you before potential issues can affect performance. Multiple pre-set reports are available. You can plan capacity based on historical usage, and run what-if scenarios as your requirements expand.
How Capacity Optimization Works
The Capacity Optimization provides four integrated functions - Overview, Reclaim, Workload Optimization, and What-If Scenarios - that give an overview of the status of all data center activity and trending. You can conduct on-the-spot analysis, including drilling down into further detail on any object to identity possible performance problems or anomalies. You can rebalance and optimize compute resources. The system further identifies underutilized workloads (virtual machines) and calculates the potential cost savings that can accrue when these resources are reclaimed to be deployed more effectively. You can interact with and manipulate data and outcomes based on your requirements.
Use the Capacity Optimization and Reclaim features to assess workload status and resource contention in data centers across your environment. You can determine time remaining until cpu, memory, or storage resources run out and realize cost savings when underutilized VMs can be reclaimed and deployed where needed.
Workload Optimization provides for moving virtual workloads and their file systems dynamically across datastore clusters within a data center or custom data center. You can potentially automate a significant portion of your data center compute and storage optimization efforts. With properly defined policies determining the threshold at which resource contention triggers an alert and automatically runs an action, a data center performs at optimum.
In addition, the What-If Analysis function- can run scenarios that help determine where additional system resources can be brought online.
You may see a data center or cluster labeled as optimized when it has few or no days remaining before CPU, memory, or storage is predicted to run out. That is because these are two different measures of data center and cluster health. A data center can be running at optimum based on policy settings for balance and consolidation, yet be almost out of resources. It is important to consider both measures when managing your environment.