This is a very good product with major use cases involving Nutanix HCI. Major customers are banking sectors including Jana Small Financials Bank, Bandhan AMC Bank, IDFC Bank, and HDFC Life Insurance. These are all our customers where we have implemented Nutanix HCI, and we have built the cloud-native.
Nutanix Prism is a bundle, similar to an example in HCI. In SimpliVity, it's completely with the HCI, and here Acropolis Operating System is present. It's a kind of OS for Nutanix Prism, AOS which we call, that is part of the best features of Nutanix Prism.
It is an intelligent management interface, Nutanix Prism. It's a single pane of glass that manages the clusters, VMs, storage, and whatever network we have will be in the single dashboard.
One customer required a single dashboard which contains the complete information for Nutanix Prism. They wanted to know the number of VMs, the storage they are using, and their switches, networking, L2 switches, L3, everything is bundled: server, storage, VMs, everything. We can showcase it in the single window.
The Prism real-time alerting and predictive analytics provide live metrics for VMs. It gives alerts every few seconds about system activities, providing complete alerts instantly. It spots performance bottlenecks such as CPU, memory, storage, IOPS, latency, etc. Everything is in the single bundle and gives alerts and real-time alerts. Also, built-in AI engines detect anomalies and send alerts instantly.
Alerts include root cause suggestions regarding Nutanix Prism, not just raw data. Additionally, it allows real-time VM operations to create power on/off, migration, snapshot or clone, and VMs instantly. Live resource reallocation and troubleshooting occurs with zero time.
The machine learning in Nutanix Prism involves data collections in real-time telemetry data. It tracks VM active usage such as CPU, memory, IOPS, latency, and host node performance, storage usage and trends, user behavior and workload. Data is collected every few seconds, creating large data sets over time. Pattern recognition and baseline creations occur as the ML model learns normal behavior of your system by analyzing historical data. For example, if X VMs usually use 50 to 60% of CPU and suddenly jump to 95%, Prism detects it as an anomaly.
Regarding predictions and forecasting features of Nutanix Prism, it handles capacity planning by predicting when you will run out of disk, CPU, or memory, and how many days are left. It predicts workload and understands the seasons or daily workload peaks and prepares suggestions ahead of time.