Artificial intelligence makes flash storage predictive
We live in a time of unparalleled opportunity where digital strategies and new applications are transforming organizations of all kinds. As infrastructure enables these strategies to come to life, a lot is expected—fast and reliable performance and always-on availability, for starters. But even though infrastructure is more complex than ever, organizations continue to spend more time and money tuning and reacting to issues. To exploit the benefits of a digital business, infrastructure must “just work” without disruption or delay to its applications or require manual intervention.
In the pursuit of better performance, many IT leaders at large and smaller enterprises, have turned to flash storage. When performance slows, they assume that storage is the likely cause and simply move to flash, hoping that will resolve the problem. Yet a recent study found that storage is at fault less than half the time (46%). Other culprits are configuration issues (28%), interoperability issues (11%), non-storage best practices impacting performance (8%), and host, compute, or virtual machine issues (7%).
Clearly, flash alone cannot ensure reliable, non-disruptive access to data or eliminate the manual attention needed. As much as IT organizations want to move their businesses ahead, infrastructure continues to hold them back through an endless cycle of break, fix, tune, repeat.
Look to predictive flash storage
Predictive flash storage represents a unique storage experience powered by artificial intelligence (AI). Deep telemetry is continuously collected across a global installed base; advanced machine learning in the cloud analyzes the data to identify irregular patterns in workloads and applications; predictive analytics automatically predicts and prevents problems; and wasted time and headaches due to disruptions and manual tuning come to an end.
With predictive flash storage, IT administrators can finally take back their time so they can focus their efforts on driving their organization forward. But it’s important to note that predictive analytics must go beyond the obvious.
This chart reflects the challenges of the data center.
As you can see, problems generally fall within this distribution: those that are simple and common (toward the left) and those that are complex and unique (toward the right). Simple and common problems, such as failed drives, are more frequent but account for only a small percentage of the pain inflicted on IT. This is the extent of most vendors’ predictive capabilities. However, the more complex and unique a problem is, the more painful—that is, difficult, time-consuming, and costly—it is to resolve.
To enable the infrastructure businesses need, organizations must be relieved of the full spectrum of problems. But how? Until now, there has not been a way. But advances in AI and machine learning are making it possible to address the complex and unique problems—the “long tail” of issues that stands in the way of an effective digital transformation.
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Must-have capabilities of artificial intelligence
Being human, we can see the present and remember a little bit about the past. However, AI sees beyond these limitations and can drive predictive insight across the storage lifecycle from planning and managing to expanding. These are the must-have capabilities of an effective AI for infrastructure:
The ability to see across the infrastructure stack. Tools providing system status per device tell only part of the story. But the ability to correlate across the multiple layers—including virtualization, compute, networks, and storage—can tell the full story.
The ability to learn. Analytics that simply report on local system metrics offer limited value because the behavior of thousands of peer systems cannot be used to aid in the detection and diagnosis of developing issues. In contrast, a global approach to data collection and analysis can pool observations from an immense variety of workloads. This teaches AI to predict problematic behavior with great accuracy.
The ability to act. The ideal state is autonomous operation without the need for human intervention. This starts with transforming vendor support—progressing from a reactive operation to one that automatically detects and resolves issues. This requires knowing what changes need to be made to avoid a problem or improve an environment, and telling the IT administrator what to do and when.
Predicting and preventing issues is a far cry from reacting to them or moving to flash. High-touch and unreliable infrastructure can blunt the edge that companies are seeking through digital business strategies. The key is to eliminate disruptions and the manual intervention that is too often required. Predictive flash storage provides the answer businesses have been looking for: visibility across the stack to anticipate and prevent problems and ensure the ideal performance and efficient resources. Such visibility can help you bring digital business strategies to life for your organization.
This article/content was written by the individual writer identified and does not necessarily reflect the view of Hewlett Packard Enterprise Company.