Great news: we are making it easier for vRealize Cloud Universal (vRCU) customers to adopt vRealize AI Cloud.

Back in VMworld 2020, we introduced the availability of VMware vRealize AI Cloud, a self-tuning service that uses reinforcement learning to continuously adapt to the changing needs of your VMware SDDC and dynamic workloads for performance optimization.

Using ML techniques in our SaaS datalake, near real-time and historical observability with on-prem data collections, vRealize AI Cloud continuously optimizes VMware infrastructure operations, starting with vSAN to reduce the need for constant monitoring.  It improves application workload performance by evaluating all possible VMware SDDC and vSAN tunables to select the optimal configuration to take action on.

What’s Coming and New!

vRealize AI Cloud, included with vRealize Operations Cloud with vRealize Cloud Universal subscription, will soon be available in its very own stand-alone UI.  This allows for flexible accessibility for vSAN customers that have not yet adopted any vRealize cloud management platform or customers that are currently using vRealize Operations on-prem editions.  So if you just recently onboarded your vRealize Cloud Universal subscription and also have vSAN clusters, go ahead and enable the vRealize AI Cloud service to see how much your VMware SDDC can benefit and improve on the optimization health score!

We will also be tech previewing the Storage Policy Genie engine that analyzes and predicts performance, capacity, and cost of change to trigger recommendations based on a net benefit score.

Today, there are many complex variables and a lack of understanding that inhibits customer utilization of Storage Policy Based Management (SPBM) policies.  Recommending and optimizing storage policies for vSAN clusters would be very desirable to maximize performance, efficiency, and scale which typically requires every VM deployed using vSAN or vVols to have an SPBM policy.

For the first scope of our tech preview, the Storage Policy Genie will make recommendations for clusters with existing workloads deployed with capabilities to:

  • Recommend changes to cluster level default policy
  • Predict performance improvements, capacity impact and cost of change
  • Present multiple alternatives with differing trade-offs and explanations

 

For more information

To learn more on vRealize AI Cloud, and other vRealize products, I invite you to check out our new VMware Pathfinder site that that covers detailed usage for:

  • Overview and Demo
  • Machine Learning Fundamentals
  • Onboarding vRealize AI Cloud
  • Tech Bytes Podcast
  • Architecture

Topics are covered at level 100 to 300 plus hands-on training!

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