Contributed article by Irfan Ahmad, Co-Founder of CloudPhysics

In 2014, Analytics will be the Only Real Differentiator Left in Storage

The game of speeds and feeds is over. It's getting harder and harder to tell the difference between storage vendors, incumbents and startups through the usual lens of capacity or latency or IOPs. There are no disruptive benefits left doing things the old way.

When you peel back the curtain, most of the special sauce in all the new approaches to storage is in the software that lives on the devices and in the packages and the analytics performed by that software. Even amongst server-side caching competitors, those that have (or will) separate from the pack do it with capabilities that are rooted in the analytics software layered into their solutions.

This software is supposed to deliver the benefits of easier management, resource pooling, granular QoS, predictable performance, automation, etc. At least that's been the promise of storage virtualization in the past and of software-defined storage today. But we're not there yet.

So what's missing? Fundamentally, what's lacking is the combination of visibility and predictive analytics that: 

• Provide you with deep visibility into what's going on
• Predict when and where you will run out of space
• Predict when and where, in terms of both applications/VMs/containers and datastores, you will experience contention
• Provide levers and automation for enforcing granular QoS for both capacity and performance
• Enable you to buy no more than what you need as well as no less

Long term, the only storage vendors--hardware or software--who will succeed are those who deliver on these promises through deep visibility and meaningful analytics.

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About the Author

In his nine years at VMware, Irfan was R&D lead and co-inventor for flagship products, including Storage DRS and Storage I/O Control. He worked extensively on interdisciplinary endeavors in memory, storage, CPU, and distributed resource management, and developed a special interest in research at the intersection of systems. He also spent several years in performance analysis and optimization, both in systems software and OS kernels. Before VMware, he worked on a software microprocessor at Transmeta.