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Automated, Self-Managed Data Centers Promise Predictable Performance Without IT Intervention

Automated, Self-Managed Data Centers Promise Predictable Performance Without IT Intervention

Keep an eye on the progress of machine-learning-based intelligent automation in the modern data center. The data center that manages itself and rarely needs admin assistance could arrive sooner than you think.

  • KEY TAKEAWAYS
  • Automation and machine learning technology is making data centers with self-managed capabilities a reality.
  • Self-managing data centers that handle routine storage tasks can optimize performance and predict future needs.
  • AI and machine learning have the potential to let your IT team focus on tasks that add real business value instead of being stuck managing infrastructure.

In a recent article for Information Management—"3 Ways Automation and Machine Learning Are Changing the Data Center”—I wrote about the coming self-managed data center. Do read the entire article, but if you’re having a tl;dr moment, here are a few highlights:

The Rise of the Self-Managed Data Center

On the heels of self-driving cars, smart home appliances, and other advances in automated technology, the next logical step is self-managing data centers where automation and machine learning handle administrative storage tasks.

Just like self-driving cars, the self-managed data center that rarely needs human intervention could be coming sooner than you think. Data centers are increasingly utilizing full self-managed capabilities, which wouldn’t be possible without automation and machine-learning technology.

Intelligent storage is a critical element of the self-managing data center. The three main storage trends helping make self-managed data centers a reality are:

  • Promising performance without intervention: With traditional storage, applications compete for resources from a fixed number of IOPS. Automation enables organizations to access IOPS resources and allows virtual machines (VMs) to employ them for other necessary purposes. Instead of saving and wasting unused IOPS, they’re available when needed, guaranteeing performance without intervention.
  • Ensuring a clear lane for every virtual machine: Machine learning and automation can analyze past performance to predict trends, giving organizations insight into what’s needed for future performance and capacity for storage-array pools. Organizations can also analyze performance trends for optimal VM placement and to predict and address poor performance on an array.
  • Optimizing the performance of storage arrays and predicting future usage trends: Machine learning can also help businesses plan for the future. Analytics can enable organizations to improve predictions and make savvier decisions about infrastructure requirements to avoid downtime. Additionally, by giving each VM its own lane, organizations will make optimal use of all their performance all the time.

Automated, self-managed data centers are becoming a reality, promising real-time, predictable performance without IT intervention. Even dense IT infrastructure that’s difficult and time-consuming to upgrade and control is becoming automated and managed through software instead of hardware. These data centers are increasingly utilizing full self-managing capabilities.

3 Ways Automation and Machine Learning Are Changing the Data Center

The full article is available at Information Management (registration required)

Read it now

Chris Colotti / May 22, 2018

Chris Colotti is a Field CTO for Tintri. In this role he speaks regularly to organizations about the design and delivery of their cloud architectures. Prior to Tintri, Chris spent a decade at VMwar...more

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