How AI Can Optimize Storage Capacity and Performance Planning

How AI Can Optimize Storage Capacity and Performance Planning

About Rahi Systems

Rahi Systems is a Global Data Center Solutions provider offering a full suite of products in physical infrastructure, storage, compute and networking. In addition, Rahi offers professional and managed services to aid customers in logistics, delivery, set-up, and ongoing support of their data center solutions.

Storage capacity planning has always been challenging, and the difficulty has increased along with escalating data volumes and IT demands. Determining available capacity and growth over time is a simple enough process using built-in system monitoring tools, log files and storage management software. However, these numbers reflect only organic growth under relatively static conditions. Most storage environments are highly complex and dynamic, making it difficult to predict capacity and performance requirements.

Storage administrators must consider a mind-boggling number of variables in order to make accurate predictions. Are there upcoming IT initiatives, such as server upgrades or application implementations, that will require storage resources? How will the addition of new users, devices and locations impact storage needs? Could storage optimization, thin provisioning or storage tiering save capacity?

These types of questions are ripe for the application of artificial intelligence (AI), which has the ability to analyze massive data sets, identify patterns and make autonomous decisions. Pure Storage is enabling customers to leverage AI for storage management, support and capacity planning with its Pure1 Meta platform.

Pure1 Meta represents a major breakthrough in AI and machine learning. It is driven by the Pure Storage global sensor network, which gathers more than 1 trillion telemetry data points from thousands of cloud-connected arrays each day. The Meta AI Engine analyzes a data lake of more than 7PB to generate Issue Fingerprints, then scans all incoming array telemetry against the library of Issue Fingerprints to identify and resolve problems in real time.

Meta also captures and analyzes thousands of variables to generate Workload DNA — a continuously refined set of profiles based upon the characteristics of more than 100,000 workloads. Using the Workload DNA generated by Pure1 Meta, customers can predict both capacity and performance and get intelligent advice on workload deployment and optimization. With clear answers to performance forecasting questions, customers don’t have to resort to workarounds that result in wasted expenses from over-provisioning or downtime from under-provisioning.

Customers can leverage Workload DNA through the Meta Workload Planner in Pure1, which was recently upgraded to include workload simulations. Storage administrators can use simulations to see what specific workloads would look like with various configuration options. This eliminates the guesswork associated with vendor data sheets and synthetic runs.

The Global Dashboard in Pure1 simplifies the management experience by providing key aggregate metrics from an entire fleet of arrays. From a single dashboard, customers can view the average load performance, performance trends for the last 24 hours and capacity consumption predictions across all arrays. Recent alerts and messages across the fleet of arrays also enables better visibility of the storage footprint.

Storage administrators seeking to optimize available resources and plan hardware purchases must continually evaluate available capacity and estimate future demands. They must also predict how changes to the storage environment will impact capacity and workload performance. Let us show you how Pure1 Meta from Pure Storage can provide clear answers to capacity planning questions, helping to reduce risk, increase resource utilization, and better plan for upgrades and expansions. CONTACT US!

 

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