Agile Big Data Clusters: DriveScale Enables Bare Metal Cloud

(Excerpt from original post on the Taneja Group News Blog)

We’ve been writing recently about the hot, potentially inevitable, trend, towards a dense IT infrastructure in which components like CPU cores and disks are not only commoditized, but deployed in massive stacks or pools (with fast matrixing switches between them). Then a layered provisioning solution can dynamically compose any desired “physical” server or cluster out of those components. Conceptually this becomes the foundation for a bare-metal cloud. DriveScale today announces their agile architecture with this approach, aimed first at solving big data multi-cluster operational challenges. 

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Scaling All Flash to New Heights – DDN Flashscale All Flash Array Brings HPC to the Data Center

(Excerpt from original post on the Taneja Group News Blog)

It’s time to start thinking about massive amounts of flash in the enterprise data center. I mean PBs of flash for the biggest, baddest, fastest data-driven applications out there. This amount of flash requires an HPC-capable storage solution brought down and packaged for enterprise IT management. Which is where Data Domain Networks (aka DDN) is stepping up. Perhaps too quietly, they have been hard at work pivoting their high-end HPC portfolio into the enterprise space. Today they are rolling out a massively scalable new flash-centric Flashscale 14KXi storage array that will help them offer complete, comprehensive single-vendor big data workflow solutions – from the fastest scratch through the biggest throughput parallel file systems into the largest distributed object storage archives.

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Data in Space: SANs Now Include Satellite Array Networks

(Excerpt from original post on the Taneja Group News Blog)

All you storage geeks and science fiction fans rejoice! If Cloud Constellation gets its way, you’ll soon be able to directly hybridize your dreary earthbound data center storage with actually above-the-clouds storage. Yep, protect your sensitive data by replicating it to true satellite storage. Only James Bond with a spare Shuttle would be able to hack those things. Just how far fetched is this idea?

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Hyperconverged Supercomputers For the Enterprise Data Center

(Excerpt from original post on the Taneja Group News Blog)

Last month NVIDIA, our favorite GPU vendor, dived into the converged appliance space. In fact we might call their new NVIDIA DGX-1 a hyperconverged supercomputer in a 4U box. Designed to support the application of GPU’s to Deep Learning (i.e. compute intensive deeply layered neural networks that need to train and run in operational timeframes over big data), this beast has 8 new Tesla P100 GPUs inside on an embedded NVLink mesh, pre-integrated with flash SSDs, decent memory, and an optimized container-hosting deep learning software stack. The best part? The price is surprisingly affordable, and can replace the 250+ server cluster you might otherwise need for effective Deep Learning.

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Server Powered Storage: Intelligent Storage Arrays Gain Server Superpowers

An IT industry analyst article published by Infostor.

At Taneja Group we are seeing a major trend within IT to leverage server and server-side resources to the maximum extent possible. Servers themselves have become commodities, and dense memory, server-side flash, even compute power continue to become increasingly powerful and cost-friendly. Many datacenters already have a glut of CPU that will only increase with newer generations of faster, larger-cored chips, denser packaging and decreasing power requirements. Disparate solutions from in-memory databases (e.g. SAP HANA) to VMware’s NSX are taking advantage of this rich excess by separating out and moving functionality that used to reside in external devices (i.e. SANs and switches) up onto the server.

Within storage we see two hot trends – hyperconvergence and software defined – getting most of the attention lately. But when we peel back the hype, we find that both are really enabled by this vastly increasing server power – in particular server resources like CPU, memory and flash are getting denser, cheaper and more powerful to the point where they are capable of hosting sophisticated storage processing capabilities directly. Where traditional arrays built on fully centralized, fully shared hardware might struggle with advanced storage functions at scale, server-side storage tends to scale functionality naturally with co-hosted application workloads. The move towards “server-siding” everything is so talked about that it seems inevitable that traditional physical array architectures are doomed.

…(read the complete as-published article there)

Big data and IoT benefit from machine learning, AI apocalypse not imminent

An IT industry analyst article published by SearchITOperations.

Suddenly, everybody is talking about machine learning, AI bots and deep learning. It’s showing up in new products to look at “call home data,” in cloud-hosted optimization services and even built into new storage arrays!

So what’s really going on? Is this something brand new or just the maturation of ideas spawned out of decades-old artificial intelligence research? Does deep learning require conversion to some mystical new church to understand it, or do our computers suddenly get way smarter overnight? Should we sleep with a finger on the power off button? But most importantly for IT folks, are advances in machine learning becoming accessible enough to readily apply to actual business problems — or is it just another decade of hype?

There are plenty of examples of highly visible machine learning applications in the press recently, both positive and negative. Microsoft’s Tay AI bot, designed to actively learn from 18 to 24 year olds on Twitter, Kik and GroupMe, unsurprisingly achieved its goal. Within hours of going live, it became a badly behaved young adult, both learning and repeating hateful, misogynistic, racist speech. Google’s AlphaGo beat a world champion at the game of Go by learning the best patterns of play from millions of past games, since the game can’t be solved through brute force computation with all the CPU cycles in the universe. Meanwhile, Google’s self-driving car hit a bus, albeit at slow speed. It clearly has more to learn about the way humans drive.

Before diving deeper, let me be clear, I have nothing but awe and respect for recent advances in machine learning. I’ve been directly and indirectly involved in applied AI and predictive modeling in various ways for most of my career. Although my current IT analyst work isn’t yet very computationally informed, there are many people working hard to use computers to automatically identify and predict trends for both fun and profit. Machine learning represents the brightest opportunity to improve life on this planet — today leveraging big data, tomorrow optimizing the Internet of Things (IoT).

Do machines really learn?

First, let’s demystify machine learning a bit. Machine learning is about finding useful patterns inherent in a given historical data set. These usually identify correlations between input values that you can observe, and output values that you’d eventually like to predict. Although precise definitions depend on the textbook, a model can be a particular algorithm with specific parameters that are tuned, or one that comes to “learn” useful patterns.

There are two broad kinds of machine learning:

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Get the most from cloud-based storage services

An IT industry analyst article published by SearchStorage.

We have been hearing about the inevitable transition to the cloud for IT infrastructure since before the turn of the century. But, year after year, storage shops quickly become focused on only that year’s prioritized initiatives, which tend to be mostly about keeping the lights on and costs low. A true vision-led shift to cloud-based storage services requires explicit executive sponsorship from the business side of an organization. But unless you cynically count the creeping use of shadow IT as an actual strategic directive to do better as an internal service provider, what gets asked of you is likely — and unfortunately — to perform only low-risk tactical deployments or incremental upgrades.

Not exactly the stuff of business transformations.

Cloud adoption at a level for maximum business impact requires big executive commitment. That amount of commitment is, quite frankly, not easy to generate.

…(read the complete as-published article there)