Nutanix Machine Learning Engine X-FIT
X-FIT is a highly scalable, application aware machine learning engine that powers Nutanix Prism capabilities such as Predictive Capacity Planning. Unlike capacity planning tools that extrapolate historical data patterns, X-Fit is application-aware and employs multiple predictive algorithms that continually compete with one another to determine the most precise forecast.
Capacity Behavior Trends
Prism’s predictive analysis engine forecasts the capacity needs of applications running on a Nutanix cluster, giving the IT team the ability to proactively plan for infrastructure needs. Prism continuously monitors usage of CPU, memory and storage across the cluster and accurately predicts when a cluster will run out of resources, known as a capacity runway.
Instead of using a single algorithm that may contain limitations such as STL, ARIMA or Theta, Prism uses X-FIT to run multiple algorithms, analyzing various models for the most accurate prediction.
Instant assessments help IT teams ensure business organizations never run out of capacity. Accurate predictive analysis enables you to plan infrastructure needs and optimize budget.
Capacity Optimization Advisor
Working closely with the Capacity Behavior Trends capability, the Capacity Optimization Advisor analyzes usage across all the applications and provides recommendations to improve capacity usage and increase runway.
Virtual Machine Analysis
The advisor detects idle VMs that can be removed to free up capacity, as well as overprovisioned VMs on which peak resource usage is exceeded so that you can reduce the capacity assigned to these virtual machines.
If no additional optimizations can be performed, the advisor will recommend specific hardware models that can be added to the cluster to increase capacity and maintain a healthy runway.
IT admins can specify a target runway and get recommendations for hardware models that should be added to increase the runway to the specified value. The advisor uses the Nutanix Sizer tool to ensure consistency.
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