Dell – Dell Technologies World 2019 – See You Soon Las Vegas

This is a quick post to let you all know that I’ll be heading to Dell’s annual conference (Dell Technologies World) this year in Las Vegas, NV. I’m looking forward to catching up with some old friends and meeting some new ones. If you haven’t registered yet but feel like that’s something you might want to do – the registration page is here. To get a feel for what’s on offer, you can check out the agenda here. I’m looking forward to hearing more about stuff like this.

I’ll also be participating in a Tech Field Day Extra event at Dell Technologies World. You can check out the event page for that here.

Massive thanks to Konstanze and Debbie from Dell for organising the “influencer” pass for me. Keep an eye out for me at the conference and surrounding events and don’t be afraid to come and say hi (if you need a visual – think Grandad Wolverine).

Random Short Take #13

Here are a few links to some random news items and other content that I found interesting. You might find them interesting too. Let’s dive in to lucky number 13.

Axellio Announces Azure Stack HCI Support

Microsoft recently announced their Azure Stack HCI program, and I had the opportunity to speak to the team from Axellio (including Bill Miller, Barry Martin, and Kara Smith) about their support for it.

 

Azure Stack Versus Azure Stack HCI

So what’s the difference between Azure Stack and Azure Stack HCI? You can think of Azure Stack as an extension of Azure – designed for cloud-native applications. The Azure Stack HCI is more for your traditional VM-based applications – the kind of ones that haven’t been refactored (or can’t be) for public cloud.

[image courtesy of Microsoft]

The Azure Stack HCI program has fifteen vendor partners on launch day, of which Axellio is one.

 

Axellio’s Take

Miller describes the Axellio solution as “[n]ot your father’s HCI infrastructure”, and Axellio tell me it “has developed the new FabricXpress All-NVMe HCI edge-computing platform built from the ground up for high-performance computing and fast storage for intense workload environments. It delivers 72 NVMe SSDS per server, and packs 2 servers into one 2U chassis”. Cluster sizes start at 4 nodes and run up to 16. Note that the form factor measurement in the table below includes any required switching for the solution. You can grab the data sheet from here.

[image courtesy of Axellio]

It uses the same Hyper-V based software-defined compute, storage and networking as Azure Stack and integrates on-premises workloads with Microsoft hybrid data services including Azure Site Recovery and Azure Backup, Cloud Witness and Azure Monitor.

 

Thoughts and Further Reading

When Microsoft first announced plans for a public cloud presence, some pundits suggested they didn’t have the chops to really make it. It seems that Microsoft has managed to perform well in that space despite what some of the analysts were saying. What Microsoft has had working in its favour is that it understands the enterprise pretty well, and has made a good push to tap that market and help get the traditionally slower moving organisations to look seriously at public cloud.

Azure Stack HCI fits nicely in between Azure and Azure Stack, giving enterprises the opportunity to host workloads that they want to keep in VMs hosted on a platform that integrates well with public cloud services that they may also wish to leverage. Despite what we want to think, not every enterprise application can be easily refactored to work in a cloud-native fashion. Nor is every enterprise ready to commit that level of investment into doing that with those applications, preferring instead to host the applications for a few more years before introducing replacement application architectures.

It’s no secret that I’m a fan of Axellio’s capabilities when it comes to edge compute and storage solutions. In speaking to the Axellio team, what stands out to me is that they really seem to understand how to put forward a performance-oriented solution that can leverage the best pieces of the Microsoft stack to deliver an on-premises hosting capability that ticks a lot of boxes. The ability to move workloads (in a staged fashion) so easily between public and private infrastructure should also have a great deal of appeal for enterprises that have traditionally struggled with workload mobility.

Enterprise operations can be a pain in the backside at the best of times. Throw in the requirement to host some workloads in public cloud environments like Azure, and your operations staff might be a little grumpy. Fans of HCI have long stated that the management of the platform, and the convergence of compute and storage, helps significantly in easing the pain of infrastructure operations. If you then take that management platform and integrate it successfully with you public cloud platform, you’re going to have a lot of fans. This isn’t Axellio’s only solution, but I think it does fit in well with their ability to deliver performance solutions in both the core and edge.

Thomas Maurer wrote up a handy article covering some of the differences between Azure Stack and Azure Stack HCI. The official Microsoft blog post on Azure Stack HCI is here. You can read the Axellio press release here.

Scale Computing and Leostream – Better Than Bert And Ernie

Scale Computing recently announced some news about a VDI solution they delivered for Illinois-based Paris Community Hospital. I had the opportunity to speak with Alan Conboy about it and thought I’d share some coverage here.

 

VDI and HCI – A Pretty Famous Pairing

When I started to write this article, I was trying to think of a dynamic duo that I could compare VDI and HCI to. Batman and Robin? Bert and Ernie? MJ and Scottie? In any case, hyper-converged infrastructure and virtual desktop infrastructure has gone well together since the advent of HCI. It’s my opinion that HCI is in a number of enterprises by virtue of the fact that a VDI requirement arose. Once HCI is introduced into those enterprise environments, folks start to realise it’s useful for other stuff too.

Operational Savings

So it makes sense that Scale Computing’s HC3 solution would be used to deliver VDI solutions at some stage. And Leostream can provide the lifecycle manager / connection broker / gateway part of the story without breaking a sweat. According to Conboy Paris Community Hospital has managed to drastically reduce its operating costs, to the point that it’s reduced its resource investment to a part-time operations staff member to manage the environment. They’re apparently saving around $1 million (US) over the next five years, meaning they can now afford an extra doctor and additional nursing staff.

HCI – It’s All In The Box

If you’re familiar with HCI, you’ll know that most of the required infrastructure comes with the solution – compute, storage, and hypervisor. You also get the ability to do cool stuff in terms of snapshots and disaster recovery via replication.

 

Thoughts

VDI solutions have proven popular in healthcare environments for a number of reasons. They generally help the organisation control the applications that are run in the (usually) security-sensitive environment, particularly at the edge. It’s also useful in terms of endpoint maintenance, and removes the requirement to deploy high end client devices in clinical environments. It also provides a centralised mechanism to ensure that critical application updates are performed in a timely fashion.

You won’t necessarily save money deploying VDI on HCI in terms of software licensing or infrastructure investment. But you will potentially save money in terms of the operational resources required for endpoint and application support. If you can then spend those savings on medical staff, that has to be a win for the average healthcare organisation.

I’m the first to admit that I don’t get overly excited about VDI solutions. I can see the potential for value in some organisations, but I tend to lose focus rapidly when people start to talk to me about this stuff. That said, I do get enthusiastic about HCI solutions that make sense, and deliver value back to the business. It strikes me that this Scale Computing and Leostream combo has worked out pretty well for Paris Community Hospital. And that’s pretty cool. For more insight, Scale Computing has published a Customer Case Study that you can read here.

Cohesity Marketplace – A Few Notes

 

Cohesity first announced their Marketplace offering in late February. I have access to a Cohesity environment (physical and virtual) in my lab, and I’ve recently had the opportunity to get up and running on some of the Marketplace-ready code, so I thought I’d share my experiences here.

 

Prerequisites

I’m currently running version 6.2 of Cohesity’s DataPlatform. I’m not sure whether this is widely available yet or still only available for early adopter testing. My understanding is that the Marketplace feature will be made generally available to Cohesity customers when 6.3 ships. The Cohesity team did install a minor patch (6.2a) on my cluster as it contained some small but necessary fixes. In this version of the code, a gflag is set to show the Apps menu. The “Enable Apps Management” in the UI under Admin – Cluster Settings was also enabled. You’ll also need to nominate an unused private subnet for the apps to use.

 

Current Application Availability

The Cohesity Marketplace has a number of Cohesity-developed and third-party apps available to install, including:

  • Splunk – Turn machine data into answers
  • SentinelOne – AI-powered threat prevention purpose built for Cohesity
  • Imanis Data – NoSQL backup, recovery, and replication
  • Cohesity Spotlight – Analyse file audit logs and find anomalous file-access patterns
  • Cohesity Insight – Search inside unstructured data
  • Cohesity EasyScript – Create, upload, and execute customised scripts
  • ClamAV – Anti-virus scans for file data

Note that none of the apps need more than Read permissions on the nominated View(s).

 

Process

App Installation

To install the app you want to run on your cluster, click on “Get App”, then enter your Helios credentials.

Review the EULA and click on “Accept & Get” to proceed. You’ll then be prompted to select the cluster(s) you want to deploy the app on. In this example, I have 5 clusters in my Helios environment. I want to install the app on C1, as it’s the physical cluster.

Using An App

Once your app is installed, it’s fairly straightforward to run it. Click on More, then Apps to access your installed apps.

 

Then you just need to click on “Run App” to get started

You’ll be prompted to set the Read Permissions for the App, along with QoS. It’s my understanding that the QoS settings are relative to other apps running on the cluster, not data protection activities, etc. The Read Permissions are applied to one or more Views. This can be changed after the initial configuration. Once the app is running you can click on Open App. In this example I’m using the Cohesity Insight app to look through some unstructured data stored on a View.

 

Thoughts

I’ve barely scratched the surface of what you achieve with the Marketplace on Cohesity’s DataPlatform. The availability of the Marketplace (and the ability to run apps on the platform) is another step closer to Cohesity’s vision of extracting additional value from secondary storage. Coupled with Cohesity’s C4000 series hardware (or perhaps whatever flavour you want to run from Cisco or HPE or the like), I can imagine you’re going to be able to do a heck a lot with this capability, particularly as more apps are validated with the platform.

I hope to do a lot more testing of this capability over the next little while, and I’ll endeavour to report back with my findings. If you’re a current Cohesity customer and haven’t talked to your account team about this capability, it’s worth getting in touch to see what you can do in terms of an evaluation. Of course, it’s also worth noting that, as with most things technology related, just because you can, doesn’t always mean you should. But if you have the use case, this is a cool capability on top of an already interesting platform.

Storage Field Day 18 – Wrap-up and Link-o-rama

Disclaimer: I recently attended Storage Field Day 18.  My flights, accommodation and other expenses were paid for by Tech Field Day. There is no requirement for me to blog about any of the content presented and I am not compensated in any way for my time at the event.  Some materials presented were discussed under NDA and don’t form part of my blog posts, but could influence future discussions.

This is a quick post to say thanks once again to Stephen and Ben, and the presenters at Storage Field Day 18. I had a super fun and educational time. For easy reference, here’s a list of the posts I did covering the events (they may not match the order of the presentations).

Storage Field Day – I’ll Be At Storage Field Day 18

Storage Field Day 18 – Day 0

Storage Field Day 18 – (Fairly) Full Disclosure

Cohesity Is (Data)Locked In

NetApp And The Space In Between

StorPool And The Death of Hardware-Defined Storage

IBM Spectrum Protect Plus – More Than Meets The Eye

Western Digital Are Keeping Composed

VAST Data – No More Tiers Mean No More Tears?

WekaIO Continues To Evolve

Datera and the Rise of Enterprise Software-Defined Storage

 

Also, here’s a number of links to posts by my fellow delegates (in no particular order). They’re all very smart people, and you should check out their stuff, particularly if you haven’t before. I’ll attempt to keep this updated as more posts are published. But if it gets stale, the Storage Field Day 18 landing page will have updated links.

 

Becky Elliott (@BeckyLElliott)

California Dreamin’ My Way to Storage Field Day 18

A VAST-ly Different Storage Story

 

Chin-Fah Heoh (@StorageGaga)

A Storage Field 18 I will go – for the fun of it

VAST Data must be something special

Catch up (fast) – IBM Spectrum Protect Plus

Clever Cohesity

Storpool – Block storage managed well

Bridges to the clouds and more – NetApp NDAS

WekaIO controls their performance destiny

The full force of Western Digital

 

Chris M Evans (@ChrisMEvans)

Podcast #3 – Chris & Matt review the SFD18 presenters

Exploiting secondary data with NDAS from NetApp

VAST Data launches with new scale-out storage platform

Can the WekaIO Matrix file system be faster than DAS?

#91 – Storage Field Day 18 in Review

 

Erik Ableson (@EAbleson)

SFD18-Western Digital

Vast Data at Storage Field Day 18

 

Ray Lucchesi (@RayLucchesi)

StorPool, fast storage for fast times

For data that never rests, NetApp NDAS

 

Jon Klaus (@JonKlaus)

My brain will be melting at Storage Field Day 18!

Faster and bigger SSDs enable us to talk about something else than IOps

How To: Clone Windows 10 from SATA SSD to M.2 SSD (& fix inaccessible boot device)

The fast WekaIO file system saves you money!

Put all your data on flash with VAST Data

 

Enrico Signoretti (@ESignoretti)

A Packed Field Day

Democratizing Data Management

How IBM is rethinking its data protection line-up

NetApp, cloudier than ever

Voices in Data Storage – Episode 10: A Conversation with Boyan Ivanov

Voices in Data Storage – Episode 11: A Conversation with Renen Hallak

Voices in Data Storage – Episode 12: A Conversation with Bill Borsari

 

Josh De Jong (@EuroBrew)

 

Matthew Leib (@MBLeib)

I Am So Looking Forward to #SFD18

#SFD18 introduces us to VAST Data

Dual Actuator drives: An interesting trend

Weka.IO and my first official briefing

Cohesity: More on the real value of data

 

Max Mortillaro (@DarkkAvenger)

Storage Field Day 18 – It’s As Intense As Storage Field Day Gets

Storage Field Day 18 – Fifty Shades of Disclosure

Cohesity – The Gold Standard in Data Management

EP17 – Storpool: Being the best in Block Based storage – with Boyan Ivanov

Developing Data Protection Solutions in the Era of Data Management

Western Digital : Innovation in 3D NAND and Low Latency Flash NAND

 

Paul L. Woodward Jr (@ExploreVM)

Storage Field Day 18, Here I Come!

 

[photo courtesy of Stephen Foskett]

Datera and the Rise of Enterprise Software-Defined Storage

Disclaimer: I recently attended Storage Field Day 18.  My flights, accommodation and other expenses were paid for by Tech Field Day. There is no requirement for me to blog about any of the content presented and I am not compensated in any way for my time at the event.  Some materials presented were discussed under NDA and don’t form part of my blog posts, but could influence future discussions.

Datera recently presented at Storage Field Day 18. You can see videos of their presentation here, and download my rough notes from here.

 

Enterprise Software-Defined Storage

Datera position themselves as delivering “Enterprise Software-Defined Storage”. But what does that really mean? Enterprise IT gives you:

  • High Performance
  • Enterprise Features
    • QoS
    • Fault Domains
    • Stretched Cluster
    • L3 Networking
    • Deduplication
    • Replication
  • HA
  • Resiliency

Software-defined storage gives you:

  • Automation
  • DC Awareness Agility
  • Continuous Availability
  • Targeted Data Placement
  • Continuous Optimisation
  • Rapid technology adoption

Combine both of these and you get Datera.

[image courtesy of Datera]

 

Why Datera?

There are some other features built in to the platform that differentiate Datera’s offering, including:

  • L3 Networking – Datera brings standard protocols with modern networking to data centre storage. Resources are designed to float to allow for agility, availability, and scalability.
  • Policy-based Operations – Datera was built from day 1 with policy controls and policy templates to easy operations at scale while maintaining agility and availability.
  • Targeted Data Placement – ensure data is distributed correctly across the physical infrastructure to meet policies around perfromance, availability, data protection while controlling cost

 

Thoughts and Further Reading

I’ve waxed lyrical about Datera’s intent-based approach previously. I like the idea that they’re positioning themselves as “Enterprise SDS”. While my day job is now at a service provider, I spent a lot of time in enterprise shops getting crusty applications to keep on running, as best as they could, on equally crusty storage arrays. Something like Datera comes along with a cool hybrid storage approach and the enterprise guys get a little nervous. They want replication, they want resiliency, they want to apply QoS policies to it.

The software-defined data centre is the darling architecture of the private cloud world. Everyone wants to work with infrastructure that can be easily automated, highly available, and extremely scalable. Historically, some of these features have flown in the face of what the enterprise wants: stability, performance, resiliency. The enterprise guys aren’t super keen on updating platforms in the middle of the day. They want to buy multiples of infrastructure components. And they want multiple sets of infrastructure protecting applications. They aren’t that far away from those software-defined folks in any case.

The ability to combine continuous optimisation with high availability is a neat part of Datera’s value proposition. Like a number of software-defined storage solutions, the ability to rapidly iterate new features within the platform, while maintaining that “enterprise” feel in terms of stability and resiliency, is a pretty cool thing. Datera are working hard to bring the best of both worlds together, and managing to deliver the agility that enterprise wants, while maintaining the availability within the infrastructure that they crave.

I’ve spoken at length before about the brutally slow pace of working in some enterprise storage shops. Operations staff are constantly being handed steamers from under-resourced or inexperienced project delivery staff. Change management people are crippling the pace. And the CIO wants to know why you’ve not moved your SQL 2005 environment to AWS. There are some very good reasons why things work the way they do (and also some very bad ones), and innovation can be painfully hard to make happen in these environments. The private cloud kids, on the other hand, are all in on the fast paced, fail fast, software-defined life. They’ve theoretically got it all humming along without a whole lot of involvement on a daily basis. Sure, they’re living on the edge (do I sound old and curmudgeonly yet?). In my opinion, Datera are doing a pretty decent job of bringing these two worlds together. I’m looking forward to seeing what they do in the next 12 months to progress that endeavour.

WekaIO Continues To Evolve

Disclaimer: I recently attended Storage Field Day 18.  My flights, accommodation and other expenses were paid for by Tech Field Day. There is no requirement for me to blog about any of the content presented and I am not compensated in any way for my time at the event.  Some materials presented were discussed under NDA and don’t form part of my blog posts, but could influence future discussions.

WekaIO recently presented at Storage Field Day 18. You can see videos of their presentation here, and download my rough notes from here. I’ve written about WekaIO before, and you can read those posts here and here.

 

WekaIO

Barbara Murphy described WekaIO Matrix as “the fastest, most scalable parallel file system for AI and technical compute workloads that ensure applications never wait for data”.

 

What They Do

So what exactly does WekaIO Matrix do?

  • WekaIO Matrix is software-defined storage solution that runs on anything from bare metal, VMs, containers, on-premises or in the cloud;
  • Fully-coherent POSIX file system that’s faster than a local file system;
  • Distributed Coding, More Resilient at Scale, Fast Rebuilds, End-to-End Data Protection; and
  • InfiniBand or Ethernet, Converged or Dedicated, on-premises or cloud.

[image courtesy of WekaIO]

 

Lots of Features

WekaIO Matrix now has a bunch of features, including:

  • Support for S3, SMB, and NFS protocols;
  • Cloud backup, Snapshots, Clones, and Snap-2-Obj;
  • Active Directory support and authentication;
  • POSIX;
  • Network High Availability;
  • Encryption;
  • Quotas;
  • HDFS; and
  • Tiering.

Flexible deployment models

  • Appliance model – compute and storage on separate infrastructure; and
  • Converged model – compute and storage on shared infrastructure.

Both models are cloud native because “[e]verybody wants the ability to be able to move to the cloud, or leverage the cloud”

 

Architectural Considerations

WekaIO is focused on delivering super fast storage via NVMe-oF, and say that NFS and SMB deliver legacy protocol support for convenience.

The Front-End

WekaIO front-ends are cluster-aware

  • Incoming read requests optimised re location and loading conditions – incoming writes can go anywhere
  • Metadata fully distributed
  • No redirects required

SR-IOV optimises network access WekaIO directly access NVMe Flash

  • Bypassing the kernel leads to better performance.

The Back-End

The WekaIO parallel clustered filesystem is

  • Optimised flash-native data placement
    • Not designed for HDD
    • No “cylinder groups” or other anachronisms – data protection (similar to EC)
    • 3-16 data drives, +2 or +4 parity drives
    • Optional hot spares – uses a “virtual” hot spare

Global namespace = hot tier + Object storage tier

  • Tiering to S3-API Object storage
    • Additional capacity with lower cost per GB
    • Files shared to object storage layer (parallelised access optimise performances, simplifies partial or offset reads)

WekaIO uses the S3-API as its equivalent of “SCSI” for HDD.

 

Conclusion and Further Reading

I like the WekaIO story. They take away a lot of the overheads associated with non-DAS storage through the use of a file system and control of the hardware. You can make DAS run really fast, but it’s invariably limited to the box that it’s in. Scale-out pools of storage still have a place, particularly in the enterprise, and WekaIO are demonstrating that the performance is there for the applications that need it. There’s a good story in terms of scale, performance, and enterprise resilience features.

Perhaps you like what you see with WekaIO Matrix but don’t want to run stuff on-premises? There’s a good story to be had with Matrix on AWS as well. You’ll be able to get some serious performance, and chances are it will fit in nicely with your cloud-native application workflow.

WekaIO continues to evolve, and I like seeing the progress they’ve been making to this point. It’s not always easy to convince the DAS folks that you can deliver a massively parallel file system and storage solution based on commodity hardware, but WekaIO are giving it a real shake. I recommend checking out Chris M. Evans’s take on WekaIO as well.

VAST Data – No More Tiers Means No More Tears?

Disclaimer: I recently attended Storage Field Day 18.  My flights, accommodation and other expenses were paid for by Tech Field Day. There is no requirement for me to blog about any of the content presented and I am not compensated in any way for my time at the event.  Some materials presented were discussed under NDA and don’t form part of my blog posts, but could influence future discussions.

VAST Data recently presented at Storage Field Day 18. You can see videos of their presentation here, and download my rough notes from here.

 

VAST Enough?

VAST Data have a solution that basically offers massive scale with Tier 1 performance, without the cost traditionally associated with Tier 1 storage.

Foundational Pieces

Some of the key pieces of the solution are technologies that weren’t commonly available until recently, including:

  • NVMe-oF – DC-scale storage protocol that enables remote NVMe devices to be accessed with direct attached performance.
  • QLC Flash – A new Flash architecture that costs less than enterprise Flash while delivering enterprise levels of performance.
  • Storage Class Memory – Persistent, NVMe memory that can be used to reliably buffer perfect writes to QLC and create large, global metadata structures to enable added efficiency.

If you read their blog post, you’ll notice that there are some interesting ideas behind the VAST Data solution, including the ideas that:

  • Flash is the only media that can be used to bring the cost of storage under what people pay today for HDD-based systems.
  • NFS and S3 can be used for applications that up until now required a level of performance that could only come from block storage.
  • Low-endurance QLC flash can be used for even the most transactional of workloads.
  • Storage computing can be disaggregated from storage media to enable greater simplicity than shared-nothing and hyper-converged architectures.
  • Data protection codes can reduce overhead to only 2% while enabling levels of resiliency 10 orders of magnitude more than classic RAID.
  • Compressed files provide evidence that data can be reduced further when viewed on a global scale.
  • Parallel storage architectures can be built without any amount of code parallelism.
  • Customers can build shared storage architectures that can compose and assign dedicated performance and security isolation to tenants on the fly.
  • One well-engineered, scalable storage system can be ‘universal’ and can enable a diverse array of workloads and requirements.

Architecture

[image courtesy of VAST Data]

  • VAST Servers – A cluster can be built with 2- 10,000 stateless servers. Servers can be collocated with applications as containers and made to auto-scale with application demand.
  • NVMe Fabric – A scalable, shared-everything cluster can be built by connecting every server and device in the cluster over commodity data center networks (Ethernet or InfiniBand).
  • NVMe Enclosures – Highly-Available NVMe Enclosures manage over one usable PB per RU. Enclosures can be scaled independent of Servers and clusters can be built to manage exabytes.

Rapid Rebuild Encoding

VAST codes accelerate rebuild speed by using a new type of algorithm that gets faster with more redundancy data. Everything is fail-in-place.

  • 150+4: 3x faster than HDD erasure rebuilds, 2.7% overhead
  • 500+10: 2x faster than HDD erasure rebuilds, 2% overhead Additional redundancy enables MTBF of over 100,000 years at scale.

Read more about that here.

Global Data Reduction

  • Data is fingerprinted in large blocks after the write is persisted in SCM
  • Fingerprints are compared to measure relative distance, similar chunks are clustered
  • Clustered data is compressed together; byte-level deltas are extracted & stored

Read more about that here.

Deployment Options

  • Full Appliance – VAST-provided turn-key appliance
  • Software-Defined – enclosures and container software
  • Software-only – run VAST SW on certified QLC hardware

 

Specifications

The storage is the VAST DF-5615 Active / Active NVMe Enclosure.

[image courtesy of VAST Data]

 

I/O Modules 2 x Active/Active IO Modules
I/O Connectivity 4 x 100Gb Ethernet or 4 x 100Gb InfiniBand
Management (optional) 4 x 1GbE
NVMe Flash Storage 44 x 15.36TB QLC Flash
NVMe Persistent Memory 12 x 1.5TB U.2 Devices
Dimensions (without cable mgmt.) 2U Rackmount

H: 3.2”, W: 17.6”, D: 37.4”

Weight 85 lbs.
Power Supplies 4 x 1500W
Power Consumption 1200W Avg / 1450W Max
Maximum Scale Up to 1,000 Enclosures

 

Compute is housed in the VAST Quad Server Chassis.

[image courtesy of VAST Data]

 

Servers 4 x Stateless VAST Servers
I/O Connectivity 8 x 50 Gb Ethernet 4 x 100 Gb InfiniBand
Management (optional) 4 x 1GbE
Physical CPU Cores 80 x 2.4 GHz
Memory 32 x 32GB 2400 MHz RDIMM
Dimensions 2U Rackmount

H: 3.42”, W: 17.24”, D: 28.86”

Weight 78 lbs.
Power Supplies 2 x 1600W
Power Consumption 750W Avg / 900W Max
Maximum Scale Up to 10,000 VAST Servers

 

Thoughts And Other Reading

One of my favourite things about the VAST Data story is the fact that they’re all in on a greenfield approach to storage architecture. Their ace in the hole is that they’re leveraging Persistent Memory, QLC and NVMe-oF to make it all work. Coupled with the disaggregated shared everything architecture, this seems to me like a fresh approach to storage. There are also some flexible options available for deployment. I haven’t seen what the commercials look like for this solution, so I can’t put my hand on my heart and tell you that this will be cheaper than a mechanical drive based solution. That said, the folks working at VAST have some good experience with doing smart things with Flash, and if anyone can make this work, they can. I look forward to reading more about VAST Data, particularly when they get some more customers that can publicly talk about what they’re doing. It also helps that my friend Howard has joined the company. In my opinion that says a lot about what they have to offer.

VAST Data have published a reasonably comprehensive overview of their soilution that can be found here. There’s also a good overview of VAST Data by Chris Mellor that you can read here. You can also read more from Chris here, and here. Glenn K. Lockwood provides one of the best overviews on VAST Data you can read here.

Western Digital Are Keeping Composed

Disclaimer: I recently attended Storage Field Day 18.  My flights, accommodation and other expenses were paid for by Tech Field Day. There is no requirement for me to blog about any of the content presented and I am not compensated in any way for my time at the event.  Some materials presented were discussed under NDA and don’t form part of my blog posts, but could influence future discussions.

Western Digital recently presented at Storage Field Day 18. You can see videos of their presentation here, and download my rough notes from here.

 

Getting Composed

Scott Hamilton (Senior Director, Product Management) spoke to the delegates about Western Digital’s vision for composable infrastructure. I’m the first to admit that I haven’t really paid enough attention to composability in the recent past, although I do know that it messes with my computer’s spell check mechanism – so it must be new and disruptive.

There’s Work To Be Done

Hamilton spoke a little about the increasingly dynamic workloads in the DC, with a recent study showing that:

  • 45% of compute hours and storage capacity are utilised
  • 70% report inefficiencies in the time required to provision compute and storage resources

There are clearly greater demands on:

  • Scalability
  • Efficiency
  • Agility
  • Performance

Path to Composability

I remember a few years ago when I was presenting to customers about hyper-converged solutions. I’d talk about the path to HCI, with build it yourself being the first step, followed by converged, and then hyper-converged. The path to Composable is similar, with converged, and hyper-converged being the precursor architectures in the modern DC.

Converged

  • Preconfigured hardware / software for a specific application and workload (think EMC Vblock or NetApp FlexPod)

Hyper-Converged

  • Software-defined with deeper levels of abstraction and automation (think Nutanix or EMC’s VxRail)

Composable

  • Disaggregated compute and storage resources
  • Shared pool of resources that can be composed and made available on demand

[image courtesy of Western Digital]

The idea is that you have a bunch of disaggregated resources that can be really used as a pool for various applications or hosts. In this architecture, there are

  • No physical systems – only composed systems;
  • No established hierarchy – CPU doesn’t own the GPU or the memory; and
  • All elements are peers on the network and they communicate with each other.

 

Can You See It?

Western Digital outlined their vision for composable infrastructure thusly:

Composable Infrastructure Vision

  • Open – open in both form factor and API for management and orchestration of composable resources
  • Scalable – independent performance and capacity scaling from rack-level to multi-rack
  • Disaggregated – true disaggregation of storage and compute for independent scaling to maximise efficiency, agility snd to reduce TCO
  • Extensible – flash, disk and future compassable entities can be independently scaled, managed and shared over the same fabric

Western Digital’s Open Composability API is also designed for DC Composability, with:

  • Logical composability of resources abstracted from the underlying physical hardware, and
  • It discovers, assembles, and composes self-virtualised resources via peer-to-peer communication.

The idea is that it enables virtual system composition of existing HCI and Next-generation SCI environments. It also

  • Future proofs the transition from hyper-converged to disaggregated architectures
  • Complements existing Redfish / Swordfish usage

You can read more about OpenFlex here. There’s also an excellent technical brief from Western Digital that you can access here.

 

OpenFlex Composable Infrastructure

We’re talking about infrastructure to support an architecture though. In this instance, Western Digital offer the:

  • OpenFlex F3000 – Fabric device and enclosure; and
  • OpenFlex D3000 – High capacity for big data

 

F3000 and E3000

The F3000 and E3000 (F is for Flash Fabric and E is for Enclosure) has the following specification:

  • Dual-port, high-performance, low-latency, fabric-attached SSD
  • 3U enclosure with 10 dual-port slots offering up to 614TB
  • Self-virtualised device with up to 256 namespaces for dynamic provisioning
  • Multiple storage tiers over the same wire – Flash and Disk accessed via NVMf

D3000

The D3000 (D is for Disk / Dense) is as follows:

  • Dual-port fabric-attached high-capacity device to balance cost and capacity
  • 1U network addressable device offering up to 168TB
  • Self-virtualised device with up to 256 namespaces for dynamic provisioning
  • Multiple storage tiers over the same wire – Flash and Disk accessed via NVMe-oF

You can get a better look at them here.

 

Thoughts and Further Reading

Western Digital covered an awful lot of ground in their presentation at Storage Field Day 18. I like the story behind a lot of what they’re selling, particularly the storage part of it. I’m still playing wait and see when it comes to the composability story. I’m a massive fan of the concept. It’s my opinion that virtualisation gave us an inkling of what could be done in terms of DC resource consumption, but there’s still an awful lot of resources wasted in modern deployments. Technologies such as containers help a bit with that resource control issue, but I’m not sure the enterprise can effectively leverage them in their current iteration, primarily because the enterprise is very, well, enterprise-y.

Composability, on the other hand, might just be the kind of thing that can free the average enterprise IT shop from the shackles of resource management ineptitude that they’ve traditionally struggled with. Much like the public cloud has helped (and created consumption problems), so too could composable infrastructure. This is assuming that we don’t try and slap older style thinking on top of the infrastructure. I’ve seen environments where operations staff needed to submit change requests to perform vMotions of VMs from one host to another. So, like anything, some super cool technology isn’t going to magically fix your broken processes. But the idea is so cool, and if companies like Western Digital can continue to push the boundaries of what’s possible with the infrastructure, there’s at least a chance that things will improve.

If you’d like to read more about the storage-y part of Western Digital, check out Chin-Fah’s post here, Erik’s post here, and Jon’s post here. There was also some talk about dual actuator drives as well. Matt Leib wrote some thoughts on that. Look for more in this space, as I think it’s starting to really heat up.