How Does Capacity Optimization Work in
VMware Aria
Operations
VMware Aria
Operations
Capacity Optimization in
VMware Aria
Operations
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.
VMware Aria
Operations
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. Capacity Optimization Using Overview, Reclaim,
Workload Optimization and What-If
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.