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

How Many IOPS Do You Need For Real-World Storage Performance?

Posted by Adam Zagorski on Aug 22, 2017 11:12:17 AM

We hear lots of hype today about millions of IOPS from someone’s latest flash offering. It’s true that these units are very fast, but the devil is in the detail and often using the products yields a much weaker performance than the marketing would lead you to expect. That’s because most vendors measure their performance using highly tweaked benchmark software. With this type of code, the devil is in the details.

A bit extreme, perhaps, but all benchmarks can be tuned for optimal performance, while we never hear about the other, slower, results.

What eats up all of that performance? In the real world, events are not as smoothly sequenced as they are in a benchmark. Data requests are not evenly spread over all the storage drives, nor are they evenly spread in time. In fact, I/O goes where the apps direct, which means some files get much more access, making the drives they are on work hard but leaving other drives nearly idling.

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Topics: NVMe, big data, Data Center, hyperconverged, storage analytics

Storage analytics impact performance and scaling

Posted by Jim O'Reilly on Jun 14, 2017 11:21:10 AM

For the last 3 decades of computer storage use, we’ve operated essentially blindfolded. What we’ve known about performance has been gleaned from artificial benchmarks such as IOMeter and guestimates of IOPS requirements during operations that depend on a sense of how fast an application is running.

The result is something like steering a car without a speedometer ... it’s a mess of close calls and inefficient operations.

On the whole, though, we muddled through. That’s no longer adequate in the storage New Age. Storage performance is stellar in comparison to those early days, with SSDs changing the level of IOPS per drive by a factor of as much as 1000X. Wait, you say, tons of IOPS…why do we have problems?

The issue is that we share much of our data across clusters of systems, while the IO demand of any given server has jumped up in response to virtualization, containers and the horsepower of the latest CPUs. In fact, that huge jump in data moving around between nodes makes driving blind impossible even for small virtualized clusters, never mind scaled-out clouds.

All of this is happening against a background of application-based resilience. System uptime is no longer measured in how long a server runs. The key measurement is how long an app runs properly. Orchestrated virtual systems recover from server failures quite quickly. The app is restarted on another instance in a different server.

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

Storage Automation In Next Generation Data Centers

Posted by Adam Zagorski on Jan 31, 2017 1:04:37 PM

Automation of device management and performance monitoring analytics are necessary to control costs of web scale data centers, especially as most organizations continually ask for their employees to do more with fewer resources.

Big Data and massive data growth are at the forefront of datacenter growth. Imagine what it takes to manage the datacenters that provide us with this information.

 

According to research conducted by Seagate, time consuming drive management activities represent the largest storage related pain points for datacenter managers. In addition to trying to manage potential failures of all of the disk drives, managers must monitor the performance of multiple servers as well. As indicated by Seagate, there are tremendous opportunities in cost savings if the timing of retiring disk drives can be optimized. Significant savings can also result from streamlining the management process.

 

While there is no such thing as a typical datacenter, for the purpose of discussion, we will assume that a typical micro-datacenter contains about 10,000 servers while a large scale data center contains on the order of 100,000 servers. In a webscale hyperconverged environment, if each server housed 15 devices (hard drives and/or flash drives), a datacenter contains anywhere from 150,000 to 1.5 million devices. That is an enormous amount of servers and devices to manage. Even if we scaled back by an order of a magnitude or two, to 50 servers and 750 drives for example, managing a data center is a daunting task.

 

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Topics: NVMe, big data, All Flash Array, hyperconverged, NVMe over Fibre

Flash Tiering: The Future of Hyper-converged Infrastructure

Posted by Adam Zagorski on Jan 12, 2017 1:04:00 PM

The Future of Hyper-converged Infrastructure

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Topics: NVMe, big data, 3D Xpoint, SSD, Intel Optane, Data Center, hyperconverged

The Art of “Storage-as-a-Service”

Posted by Jim O'Reilly on Jan 9, 2017 2:24:50 PM

The Art of “Storage-as-a-Service”

Most enterprise datacenters are today considering the hybrid cloud model for their future deployments. Agile and flexible, the model is expected to yield higher efficiencies than traditional setups, while allowing a datacenter to be sized to average, as opposed to peak, workloads.

In reality, achieving portability of apps between clouds and reacting rapidly to workload increases both run up against a data placement problem. The agility idea fails when data is in the wrong cloud when a burst is needed. This is exacerbated by the new containers approach, which can start up a new instance in a few milliseconds.

Data placement is in fact the most critical issue in hybrid cloud deployment. Pre-emptively providing data in the right cloud prior to firing up the instances that use it is the only way to assure adequate those expected efficiency gains.

A number of approaches have been tried, with varying success, but none are truly easy to implement and all require heavy manual intervention. Let’s look at some of these approaches:

  1. Sharding the dataset – By identifying the hottest segment of the dataset (e.g. Names beginning with S), this approach places a snapshot of those files in the public cloud and periodically updates it. When a cloudburst is needed, locks for any files being changed are passed over to the public cloud and the in-house versions of the files are blocked from updating. The public cloud files are then updated and the locks cleared.
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Topics: NVMe, autotiering, big data, SSD, hyperconverged

Hot Trends In Storage

Posted by Adam Zagorski on Dec 13, 2016 2:02:41 PM

Storage continues to be a volatile segment of IT. Hot areas trending in the news this month include NVMe over Fibre Channel, which is being hyped heavily now that the Broadcom acquisition of Brocade is a done deal. Another hot segment is the hyper-converged space, complimented by activity in software-defined storage from several vendors.

Flash is now running ahead of enterprise hard drives in the market, contributing to foundry changeovers to 3D NAND to temporarily put upward pressure on SSD pricing. High-performance storage solutions built on COTS platforms have been announced, too, which will create more pressure to reduce appliance prices.

Let’s cover these topics and more in detail:

  1. NVMe over Fibre-Channel is in full hype mode right now. This solution is a major step away from traditional FC insofar as it no longer encapsulates the SCSI block-IO protocol. Instead, it uses a now-standard direct-memory access approach to reduce overhead and speed up performance significantly.
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Topics: NVMe, SSD, hyperconverged, NVMe over Fibre

Delivering Data Faster

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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.

 

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