Enmotus Blog

A New Age Storage Stack

Posted by Jim O'Reilly on Jun 4, 2018 3:42:54 PM

For over three decades, we’ve lived with a boring truth. Disk drive performance was stuck in a rut, only doubling over all that time. One consequence was that storage architecture became frozen, with little real innovation. RAID added a boost, but at a high price. In fact, we didn’t get a break until SSDs arrived on the scene.

SSDs really upset the applecart. Per drive performance increased 1000X in just a few years and all bets were off at that point. Little did we realize that the potential of SSDs reached into stratospheric levels of millions of IOPS per drive.

All of this performance broke the standard SCSI model of the storage stack in the operating system. An interrupt-driven, verbose stack with up to seven levels of address translation just doesn’t cut the I/O rate needed. The answer is the NVMe stack, which consolidates I/O’s and interrupts efficiently and uses the power of RDMA to reduce round-trip counts and overhead dramatically. IOPS rates in excess of 20M IOPS have been demonstrated and there is still room to speed up the protocol.

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Topics: NVDIMM, enmotus, NVMe over Fibre, NVMe, autotiering, hyperconverged, data analytics

Evolution of Storage - Part 2

Posted by Jim O'Reilly on May 10, 2018 10:30:51 AM

Part 2 …The Drive

Over time, the smarts in storage have migrated back and forth between the drive and the host system. Behind this shifting picture are two key factors. First, the intelligence of a micro-controller chip determines what a drive can do, while secondly, the need to correct media errors establishes what a drive must do.

Once SCSI hit the market, the functionality split between host and drive essentially froze and continued so for nearly 3 decades. The advent of new error-correction needs for SSDs, combined with the arrival of ARM CPUs that are both cheap and powerful, making function-shifting once again interesting.

Certainly, some of the new compute power goes to sophisticated multi-tier error correction to compensate for the wear out of QLC drives or the effects of media variations, but a 4-core or 8-core

 ARM still has a lot of unused capability. We’ve struggled to figure out how to use that power for meaningful storage functions and that’s led to a number of early initiatives.

The first to bat was Seagate’s Kinetic drive. Making a play for storing “Big Data” in a more native form, Kinetic adds a key/data store to its interface, replacing the traditional block access altogether. While the Kinetic interface is an open standard and free to emulate, no other vendor has yet jumped on the bandwagon and Seagate’s sales are small.

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Topics: software defined storage, NVDIMM, SDS, enmotus, NVMe

Evolution of Storage Software

Posted by Adam Zagorski on Apr 4, 2018 12:37:47 PM

Part 1 … the server and cluster


Since time immemorial, we have used the SCSI-based file stack to define how we talk to drives. Mature, but very verbose, it was an ideal match to single-core CPUs and slow interfaces to very slow hard drives. With this stack, it was perfectly acceptable to initiate an I/O and then swap processes, since the I/O took many milliseconds to complete.

The arrival of flash drives upset this applecart completely. IOPS per drive grew by 1000X in short order and neither SCSI-based SAS nor SATA could keep up. The problem continues to get worse, with the most recent flash card leader, Smart IOPS, delivering 1.7 million IOPS, a 10-fold further increase.

The industry’s answer to this performance issue is replacing SAS and SATA with PCIe and the protocol with NVMe. This gives us a solution where multiple ring-buffers contain queues of storage operations, with these queues being contexted to cores or even apps. This allows a bunch of operations to be pulled from the queue and processed by the drive using RDMA techniques. On the return side, response queues are likewise built up and serviced by the appropriate host.  Interrupts are concatenated so that one interrupt services many responses.

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Topics: NVMe, software defined storage, Intel Optane, big data

A.I. For Storage

Posted by Jim O'Reilly on Dec 18, 2017 2:12:46 PM

As we saw in the previous part of this two-part series, “Storage for A.I.”, the performance demands of A.I. will combine with technical advances in non-volatile memory to dramatically increase performance and scale within the storage pool and also move addressing of data to a much finer granularity, the byte level rather than 4KB block. This all creates a manageability challenge that must be resolved if we are to attain the potential of A.I. systems (and next-gen computing in general).

Simply put, storage is getting complex and will become ever more so as we expand the size and use of Big Data. Rapid and agile monetization of data will be the mantra of the next decade. Consequentially, the IT industry is starting to look for ways to migrate from today’s essentially manual storage management paradigms to emulate and exceed the automation of control demonstrated in 

public clouds.

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Topics: Data Center, NVDIMM, NVMe, NVMe over Fibre, artificial intelligence, enmotus, data analytics

Storage for Artificial Intelligence

Posted by Jim O'Reilly on Dec 4, 2017 1:17:42 PM

It’s not often I can write about two dissimilar views of the same technology, but recent moves in the industry on the A.I. front mean that not only does storage need to better align with A.I. needs than any traditional storage approach, but the rise of software-defined storage concepts makes A.I. an inevitable choice for solving advanced problems. The result, this article on “Storage for A.I.” and the second part of the story on “A.I for Storage”.

The issue is delivery. A.I. is very data hungry. The more data A.I. sees, the better its results. Traditional storage, the world of RAID and SAN, iSCSI and arrays of drives, is a world of bottlenecks, queues and latencies. There’s the much-layered file stack in the requesting server, protocol latency, and then the ultimate choke point, the array controller.

That controller can talk to 64 drives or more, via SATA or SAS, but typically only has output equivalent to maybe 8 SATA ports. This didn’t matter much with HDDs, but SSDs can deliver data much faster than spinning rust and so we have a massive choke point just in reducing streams to the array output ports’ capability.

There’s more! That controller is in the data path and data is queued up in its memory, adding latency. Then we need to look at the file stack latency. That stack is a much-patched solution with layer upon layer of added functionality and virtualization. In fact, the “address” of a block of data is transformed no less than 7 times before it reaches the actual bits and bytes on the drive. This was very necessary for the array world, but solid state drives are fundamentally different and simplicity is a possibility.

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Topics: artificial intelligence, machine learning, NVMe, NVDIMM, SSD

Enmotus and Micron Demonstrate NVDIMM/NVMe Storage Solution at SC17

Posted by Adam Zagorski on Nov 13, 2017 2:02:49 PM

Extreme Performance Solution Designed To Accelerate Applications Requiring Uncompromised IOPS and Latency

 

Aliso Viejo, Ca. – Nov 13, 2017 - Enmotus, the market leader in Storage Automation and Analytics software (SAA), in conjunction with Micron Technology, Inc. (Nasdaq:MU), announced an industry-first demonstration of a fully automated tiered volume consisting of NVDIMMs and NVMe flash technology. The demo will be showcased in Micron’s booth #1963 at the SC17 Conference being held in Denver, Colorado November 12-17, 2017.

 

“Enmotus’ FuzeDrive Virtual SSD Software combines the NVDIMMs and NVMe flash into a single, fully automated virtual volume,” said Andy Mills, CEO of Enmotus. “The software identifies the active data set of applications, and dynamically allocates the appropriate storage resources to optimize performance,” added Mills.

 

The combination of the Enmotus Virtual SSD software and Micron NVDIMM and NVMe technology achieves nearly 2 million IOPS in the high-density, performance storage solution targeted at HPC environments.

 

Demo configuration:

 

About Enmotus:

Enmotus develops software device virtualization and visualization solutions for data center, and web scale servers. Our products enable OEMs, system builders and IT managers to easily virtualize multi-vendor PCIe SSD and SAS/SATA storage devices in servers and storage appliances. Utilizing spatial usage statistics, the software determines the active data set, which enables allocating flash dynamically to the applications that require it. For more information, please visit www.enmotus.com or contact us at info@enmotus.com.

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Topics: NVDIMM, Dell EMC, Micron, NVMe, 2 Million IOPS

Information Storage – A truly novel concept

Posted by Jim O'Reilly on Oct 17, 2017 9:37:29 AM

When you see “storage” mentioned it’s often “data storage”. The implication is that there is nothing in the “data” that is informational, which even at a verbatim read is clearly no longer true. Open the storage up, of course, and the content is a vast source of information, both mined and unmined, but our worldview of storage has been to treat objects as essentially dumb, inanimate things.

This 1970’s view of storage’s mission is beginning to change. The dumb storage appliance is turning into smart software-defined storage services running in virtual clusters or clouds, with direct access to storage drives. As this evolution to SDS has picked up momentum, pioneers in the industry are taking a step beyond and looking at ways to extract useful information from what is stored and convert it to new ways to manage the information lifecycle, protect integrity and security and provide guidance that is information-centric to assist processing and guide the other activities around the object.

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Topics: Data Center, big data, SSD

Using advanced analytics to admin a storage pool

Posted by Adam Zagorski on Sep 25, 2017 1:43:50 PM

Manual administration of a virtualized storage pool is impossible. The pace of change and the complexity of the information returned from any metrication is too complex for a human to understand and respond in anything close to an acceptable timeframe.

Storage analytics sort through the metrics from the storage pool and distil useful information from a tremendous amount of near-real-time data. The aim of the analytics is to present information about a resolvable issue in a form that is easy to understand, uncluttered by extraneous data on non-important events.

Let’s take detecting a failed drive as an example. In the early days of storage, understanding a drive failure involved a whole series of CLI steps to get to the drive and read status data in chunks. This was often complicated by the drive being in a RAID array drive-set. This approach worked for the 24 drives on your server, but what happens when we have 256 drives and 10 RAID boxes, or 100 RAID boxes…get the problem?

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Topics: All Flash Array, Data Center, NVMe, big data, cloud storage, data analytics

Car Wrecks and Crashing Computers

Posted by Jim O'Reilly on Sep 13, 2017 12:05:33 PM

We are just starting the self-driving car era. It’s a logical follow-on to having GPS and always-connected vehicles, but we are still in the early days of evolution. Even so, it’s a fair bet that a decade from now, most, if not all, vehicles will have self-driving capability.

What isn’t clear is what it will look like. Getting from point A to point B is easy enough (GPS), and avoiding hitting anything else seems to be in the bag, too. What isn’t figured is how to stop those awful traffic jams. I live in Los Angeles and a 3-hour commute Friday afternoon is commonplace. In fact, Angelinos typically spend between 6 and 20 hours a week in their cars, with the engine running, gas being guzzled and their tempers being frayed!

It’s particularly true in LA that each car usually has a single occupant, so that’s a lot of gas, metal and pavement space for a small payload. What this leads us to is the idea of

  1. Automating car control and centralizing routing. This would allow, via a cloud app, load-balancing the roads and routing around slowdowns
  2. Making the vehicles single or dual seater electric mini-cars
  3. Using the Mini-cars to pack more effective lanes and move cars closer together
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Topics: big data, data analytics, cloud storage

Optimizing Dataflow in Next-Gen Clusters

Posted by Jim O'Reilly on Sep 6, 2017 10:57:55 AM

We are on the edge of some dramatic changes in computing infrastructure. New packaging methods, ultra-dense SSDs and high core counts will change what a cluster looks like. Can you imagine a 1U box having 60 cores and a raw SSD capacity of 1 petabyte? What about drives using 25GbE interfaces (with RDMA and NVMe over Fabrics), accessed by any server in the cluster?

Consider Intel’s new “ruler” drive, the P4500 (shown below with a concept server). It’s easy to see 32 to 40 TB of capacity per drive, which means that the 32 drives in their

concept storage appliance give a petabyte of raw capacity (and over 5PB compressed). It’s a relatively easy step to see those two controllers replaced by ARM-based data movers which reduce system overhead dramatically and boost performance nearer to available drive performance, but the likely next step is to replace the ARM units with merchant class GbE switches and talk directly to the drives.

I can imagine a few of these units at the top of each rack with a bunch of 25/50 GbE links to physically compact, but powerful, servers (2 or 4 per rack U) which use NVDIMM as close-in persistent memory.

The clear benefit is that admins can react to the changing needs of the cluster for performance and bulk storage independently of the compute horsepower deployed. This is very important as storage moves from low-capacity structured to huge capacity big-data unstructured.

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Topics: All Flash Array, Data Center, Intel Optane, NVMe over Fibre, data analytics

Delivering Data Faster

Accelerating cloud, enterprise and high performance computing

Enmotus FuzeDrive accelerates your hot data when you need it, stores it on cost effective media when you don't, and does it all automatically so you don't have to.

 

  • Visual performance monitoring
  • Graphical managment interface
  • Best in class performance/capacity

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