NetApp Announces NetApp ONTAP AI

As a member of NetApp United, I had the opportunity to sit in on a briefing from Mike McNamara about NetApp‘s recently announced AI offering, the snappily named “NetApp ONTAP AI”. I thought I’d provide a brief overview here and share some thoughts.

 

The Announcement

So what is NetApp ONTAP AI? It’s a “proven” architecture delivered via NetApp’s channel partners. It’s comprised of compute, storage and networking. Storage is delivered over NFS. The idea is that you can start small and scale out as required.

Hardware

Software

  • NVIDIA GPU Cloud Deep Learning Stack
  • NetApp ONTAP 9
  • Trident, dynamic storage provisioner

Support

  • Single point of contact support
  • Proven support model

 

[image courtesy of NetApp]

 

Thoughts and Further Reading

I’ve written about NetApp’s Edge to Core to Cloud story before, and this offering certainly builds on the work they’ve done with big data and machine learning solutions. Artificial Intelligence (AI) and Machine Learning (ML) solutions are like big data from five years ago, or public cloud. You can’t go to any industry event, or take a briefing from an infrastructure vendor, without hearing all about how they’re delivering solutions focused on AI. What you do with the gear once you’ve bought one of these spectacularly ugly boxes is up to you, obviously, and I don’t want to get in to whether some of these solutions are really “AI” or not (hint: they’re usually not). While the vendors are gushing breathlessly about how AI will conquer the world, if you tone down the hyperbole a bit, there’re still some fascinating problems being solved with these kinds of solutions.

I don’t think that every business, right now, will benefit from an AI strategy. As much as the vendors would like to have you buy one of everything, these kinds of solutions are very good at doing particular tasks, most of which are probably not in your core remit. That’s not to say that you won’t benefit in the very near future from some of the research and development being done in this area. And it’s for this reason that I think architectures like this one, and those from NetApp’s competitors, are contributing something significant to the ongoing advancement of these fields.

I also like that this is delivered via channel partners. It indicates, at least at first glance, that AI-focused solutions aren’t simply something you can slap a SKU on and sells 100s of. Partners generally have a better breadth of experience across the various hardware, software and services elements and their respective constraints, and will often be in a better position to spend time understanding the problem at hand rather than treating everything as the same problem with one solution. There’s also less chance that the partner’s sales people will have performance accelerators tied to selling one particular line of products. This can be useful when trying to solve problems that are spread across multiple disciplines and business units.

The folks at NVIDIA have made a lot of noise in the AI / ML marketplace lately, and with good reason. They know how to put together blazingly fast systems. I’ll be interested to see how this architecture goes in the marketplace, and whether customers are primarily from the NetApp side of the fence, from the NVIDIA side, or perhaps both. You can grab a copy of the solution brief here, and there’s an AI white paper you can download from here. The real meat and potatoes though, is the reference architecture document itself, which you can find here.

Puppet Announces Puppet Discovery, Can Now Find and Manage Your Stuff Everywhere

Puppet recently wrapped up their conference, PuppetConf2017, and made some product announcements at the same time. I thought I’d provide some brief coverage of one of the key announcements here.

 

What’s a Discovery Puppet?

No, it’s Puppet Discovery, and it’s the evolution of Puppet’s focus on container and cloud infrastructure discovery, and the result of feedback from their customers on what’s been a challenge for them. Puppet describe it as “a new turnkey approach to traditional and cloud resource discovery”.

It also provides:

  • Agentless service discovery for AWS EC2, containers, and physical hosts;
  • Actionable views across the environment; and
  • The ability to bring unmanaged resources under Puppet management.

Puppet Discovery currently allows for discovery of VMware vSphere VMs, AWS and Azure resources, and containers, with support for other cloud vendors, such as Google Cloud Platform, to follow.

 

Conclusion and Further Reading

Puppet have been around for some time and do a lot of interesting stuff. I haven’t covered them previously on this blog, but that doesn’t mean they’re not doing interesting stuff. I have a lot of customers leveraging Puppet in the wild, and any time companies make the discovery, management and automation of infrastructure easier I’m all for it. I’m particularly enthusiastic about the hybrid play, as I agree with Puppet’s claim that a lot of these types of solutions work particularly well on static, internal networks but struggle when technologies such as containers and public cloud come into play.

Just like VM sprawl before it, cloud sprawl is a problem that enterprises, in particular, are starting to experience with more frequency. Tools like Discovery can help identify just what exactly has been deployed. Once users have a better handle on that, they can start to make decisions about what needs to stay and what should go. I think this is key to good infrastructure management, regardless of whther you were jeans and a t-shirt to work or prefer a suit and tie.

The press release for Puppet Discovery can be found here. You can apply to participate in the preview phase here. There’s also a blog post covering the announcement here.

Tintri Announces New Scale-Out Storage Platform

I’ve had a few briefings with Tintri now, and talked about Tintri’s T5040 here. Today they announced a few enhancements to their product line, including:

  • Nine new Tintri VMstore T5000 all flash models with capacity expansion capabilities;
  • VM Scale-out software;
  • Tintri Analytics for predictive capacity and performance planning; and
  • Two new Tintri Cloud offerings.

 

Scale-out Storage Platform

You might be familiar with the T5040, T5060 and T5080 models, with the Tintri VMstore T5000 all-flash series being introduced in August 2015. All three models have been updated with new capacity options ranging from 17 TB to 308 TB. These systems use the latest in 3D NAND technology and high density drives to offer organizations both higher capacity and lower $/GB.

Tintri03_NewModels

The new models have the following characteristics:

  • Federated pool of storage. You can now treat multiple Tintri VMstores—both all-flash and hybrid-flash nodes—as a pool of storage. This makes management, planning and resource allocation a lot simpler. You can have up to 32 VMstores in a pool.
  • Scalability and performance. The storage platform is designed to scale to more than one million VMs. Tintri tell me that the  “[s]eparation of control flow from data flow ensures low latency and scalability to a very large number of storage nodes”.
  • This allows you to scale from small to very large with new and existing, all flash and hybrid, partially or fully populated systems.
  • The VM Scale-out software works across any standard high performance Ethernet network, eliminating the need for proprietary interconnects. The VM Scale-out software automatically provides best placement recommendation for VMs.
  • Scale compute and storage independently. Loose coupling of storage and compute provides customers with maximum flexibility to scale these elements independently. I think this is Tintri’s way of saying they’re not (yet) heading down the hyperconverged path.

 

VM Scale-out Software

Tintri’s new VM Scale-out Software (*included with Tintri Global Center Advanced license) provides the following capabilities:

  • Predictive analytics derived from one million statistics collected every 10 minutes from 30 days of history, accounting for peak loads instead of average loads, providing (according to Tintri) for the most accurate predictions. Deep workload analysis identifies VMs that are growing rapidly and applies sophisticated algorithms to model the growth ahead and avoid resource constraints.
  • Least-cost optimization based on multi-dimensional modelling. Control algorithm constantly optimizes across the thousands of VMs in each pool of VMstores, taking into account space savings, resources required by each VM, and the cost in time and data to move VMs, and makes the least-cost recommendation for VM migration that optimizes the pool.
  • Retain VM policy settings and stats. When a VM is moved, not only are the snapshots moved with the VM, the stastistics,  protection and QoS policies migrate as well using efficient compressed and deduplicated replication protocol.
  • Supports all major hypervisors.

Tintri04_ScaleOut

You can check out a YouTube video on Tintri VM Scale-out (covering optimal VM distribution) here.

 

Tintri Analytics
Tintri has always offered real-time, VM-level analytics as part of its Tintri Operating System and Tintri Global Center management system. This has now been expanded to include a SaaS offering of predictive analytics that provides organizations with the ability to model both capacity and performance requirements. Powered by big data engines such as Apache Spark and Elastic Search, Tintri Analytics is capable of analyzing stats from 500,000 VMs over several years in one second.  By mining the rich VM-level metadata, Tintri Analytics provides customers with information about their environment to help them make better decisions about applications’ behaviours and storage needs.

Tintri Analytics is a SaaS tool that allows you to model storage needs up to 6 months into the future based on up to 3 years of historical data.

Tintri01_Analytics

Here is a shot of the dashboard. You can see a few things here, including:

  • Your live resource usage for your entire footprint up to 32 VMstores;
  • Average consumption per VM (bottom left); and
  • The types of applications that are your largest consumers of Capacity, Performance and Working Set (bottom center).

Tintri02_Analytics

Here you can see exactly how your usage of capacity, performance and working set have been trending over time. You can see also when you can expect to run out of these resources (and which is on the critical path). It also provides the ability to change the timeframe to alter the projections, or drill into specific application types to understand their impact on your footprint.

There are a number of videos covering Tintri Analytics that I think are worth checking out:

 

Tintri Cloud Suites

Tintri have also come up with a new packaging model called “Tintri Cloud”. Aimed at folks still keen on private cloud deployments, Tintri Cloud combines the Tintri Scale-out platform and the all-flash VMstores.

Customers can start with a single Tintri VMstore T5040 with 17 TB of effective capacity and scale out to the Tintri Foundation Cloud with 1.2 PB in as few as 8 rack units. Or they can grow all the way to the Tintri Ultimate Cloud, which delivers a 10 PB cloud-ready storage infrastructure for up to 160,000 VMs, delivering over 6.4 million IOPS in 64 RU for less than $1/GB effective. Both the Foundation Cloud and Ultimate Cloud include Tintri’s complete set of software offerings for storage management, VM-level analytics, VM Scale-out, replication, QoS, and lifecycle management.

 

Further Reading and Thoughts

There’s another video covering setting policies on groups of VMs in Tintri Global Center here. You might also like to check out the Tintri Product Launch webinar.

Tintri have made quite a big deal about their “VM-aware” storage in the past, and haven’t been afraid to call out the bigger players on their approach to VM-centric storage. While I think they’ve missed the mark with some of their comments, I’ve enjoyed the approach they’ve taken with their own products. I’ve also certainly been impressed with the demonstrations I’ve been given on the capability built into the arrays and available via Global Center. Deploying workload to the public cloud isn’t for everyone, and Tintri are doing a bang-up job of going for those who still want to run their VM storage decoupled from their compute and in their own data centre. I love the analytics capability, and the UI looks to be fairly straightforward and informative. Trending still seems to be a thing that companies are struggling with, so if a dashboard can help them with further insight then it can’t be a bad thing.