Back To The Future With Tintri

Disclaimer: I recently attended Storage Field Day 21.  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.

Tintri recently presented at Storage Field Day 21. You can see videos of the presentation here, and download my rough notes from here.



Remember Tintri? The company was founded in 2008, fell upon difficult times in 2018, and was acquired by DDN. It’s still going strong, and now offers a variety of products under the Tintri brand, including VMstore, IntelliFlash, and NexentaStor. I’ve had exposure to all of these different lines of business over the years, and was interested to see how it was all coming together under the DDN acquisition.


Does Your Storage Drive Itself?

Ever since I got into the diskslinger game, self-healing infrastructure has been talked about as the next big thing in terms of reducing operational overheads. We build this stuff, can teach it how to do things, surely we can get it to fix itself when it goes bang? As those of you who’ve been in the industry for some time would likely know, we’re still some ways off that being a reality across a broad range of infrastructure solutions. But we do seem closer than we were a while ago.

Autonomous Infrastructure

Tintri spent some time talking about what it was trying to achieve with its infrastructure by comparing it to autonomous vehicle development. If you think about it for a minute, it’s a little easier to grasp the concept of a vehicle driving itself somewhere, using a lot of telemetry and little computers to get there, than it is to think about how disk storage might be able to self-repair and redirect resources where they’re most needed. Of most interest to me was the distinction made between analytics and intelligence. It’s one thing to collect a bunch of telemetry data (something that storage companies have been reasonably good at for some time now) and analyse it after the fact to come to conclusions about what the storage is doing well and what it’s doing poorly. It’s quite another thing to use that data on the fly to make decisions about what the storage should be doing, without needing the storage manager to intervene.

[image courtesy of Tintri]

If you look at the various levels of intelligence, you’ll see that autonomy eventually kicks in and the concept of supervision and management moves away. The key to the success of this is making sure that your infrastructure is doing the right things autonomously.

So What Do You Really Get?

[image courtesy of Tintri]

You get an awful lot from Tintri in terms of information that helps the platform decide what it needs to do to service workloads in an appropriate fashion. It’s interesting to see how the different layers deliver different outcomes in terms of frequency as well. Some of this is down to physics, and time to value. The info in the cloud may not help you make an immediate decision on what to do with your workloads, but it will certainly help when the hapless capacity manager comes asking for the 12-month forecast.



I was being a little cheeky with the title of this post. I was a big fan of what Tintri was able to deliver in terms of storage analytics with a virtualisation focus all those years ago. It feels like some things haven’t changed, particularly when looking at the core benefits of VMstore. But that’s okay, because all of the things that were cool about VMstore back then are still actually cool, and absolutely still valuable in most enterprise storage shops. I don’t doubt that there are VMware shops that have definitely taken up vVols, and wouldn’t get as much out of VMstore as those shops running oldey timey LUNs, but there are plenty of organisations that just need storage to host VMs on, storage that gives them insight into how it’s performing. Maybe it’s even storage that can move some stuff around on the fly to make things work a little better.

It’s a solid foundation upon which to add a bunch of pretty cool features. I’m not 100% convinced that what Tintri is proposing is the reality in a number of enterprise shops (have you ever had to fill out a change request to storage vMotion a VM before?), but that doesn’t mean it’s not a noble goal, and certainly one worth pursuing. I’m a fan of any vendor that is actively working to take the work out of infrastructure, and allowing people to focus on the business of doing business (or whatever it is that they need to focus on). It looks like Tintri has made some really progress towards reducing the overhead of infrastructure, and I’m keen to see how that plays out across the product portfolio over the next year or two.



Dell Technologies World 2018 – Wednesday General Session – Technologies & Trends That Are Changing The World – Rough Notes

Disclaimer: I recently attended Dell Technologies World 2018.  My flights, accommodation and conference pass were paid for by Dell Technologies via the Press, Analysts and Influencers program. 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.

<Eddie Vedder Voice> This one’s called longest title in the Dell Technologies World catalogue </Eddie Vedder Voice>

Here are my rough notes from Wednesday’s General Session – Technologies & Trends That Are Changing The World – at Dell Technologies World.

Allison Dew takes the stage. Data is the rocket fuel of our economy. The people in this room have always known that. We’re seeing emerging technologies like AI, ML, IoT, Blockchain becoming more mainstream. That’s enabling us to really unlock the power and the possibility of all that data. Data as the lifeblood of the economy – the next business revolution.

Morgan Stanley did some research and have suggested that “[t]he data era has become investable“. For example:

  • Walmart have reduced the time it takes to identify the source of food contamination from 7 days to seconds; and
  • NASDAQ can now identify potential fraud in realtime.

Using data combined with emerging technology to really unlock business potential. This will drive top-line growth, reduce costs, improving the bottom line. PWC think AI will contribute $16 Trillion to the global economy.

Dew then introduces Ashton Kutcher as an entrepreneur, and someone who asks “[h]ow do we use data for social good?”.

AK: There’s a lot of people here.

AD: It’s 10am on a Wednesday morning in Vegas, yeah.

AK: When I’m in Vegas I’m not usually up at 10.

AD: So you’re primarily known as an actor, and also a successful investor. How did you get into that?

AK: I was acting on “That 70s Show“. I was a Biochem engineering major in college. I left and became a model. I had a lot of extra time on the show as it was a large cast. I started producing reality TV. I started a production company. I started to see the buffering speeds for video improving on-line. We pivoted to be a digital media company. Needed to be able to measure and quantify distribution. Trying to quantify creativity. Elements in content that would increase distribution. Platforms that could measure that. If you could measure it you could improve it. Technology was a better way than hiring people. Started investing in foursquare, Skype, … Partnered with some people and started a VC firm. Started a new fund called Sound Ventures.

AD: What do you look for in terms of characteristics? In terms of transformation?

AK: It starts with a counter-intuitive thesis of some sort. The more I invest the more I realise the value of the company. You need to look at things in a different way. There are already so many giants in the market. If it’s really different, they might be able to get a head start.

AD: Example?

AK: Airbnb is counter-intuitive. Letting a stranger stay at your house is weird. But it works. They realised quickly from a personal data set that it worked. As the clients grow, the trust in the platform grows and becomes more valuable. Counter-intuitive ideology is core and key. Built off some data set that someone doesn’t have. It’s all about the team, the founders, their expertise. They have to have some edge. Everyone’s going to try and copy them. They need to be determined to walk though walls on behalf of their idea.

AD: So you spend some time getting to know them?

AK: I try to work out if I would I go work for that person. I don’t have a lot of spare time, but during the cycle I sometimes consider quitting my job and going to work for them.

AD: We talk about unintended consequences. Who knew the doorbell industry needed to be disrupted?

AK: I did. I met with the Ring founder when it was still a hardware play. I really don’t need this. Now I have one. That’s the other trick. When it was doormat it was really immature. And hardware is really hard, as you all know. I wasn’t a huge Amazon user at the time. I don’t buy very much. I didn’t understand how or if the next consumer was going to benefit from the previous user’s behaviour.

AD: Putting IoT on ice machines. Sometimes its about protection – insurance. Suddenly those ice trucks started using less salt. Is there anything you’re looking at now?

AK: I made a small investment in a company that does motorised scooters. People are upset because these scooters are ending up everywhere. How many times do you see cars everywhere and think, wow, these cars are a problem? Wouldn’t it be better if it was scooters? This is one of those early fights and people are going to realise that a few less cars on the road is good. Radar – using RFID tags – very localised. Every product in your store. Inventory control and checkouts. A lot of stuff in healthcare I’m interested in. Data with doctors – assisted AI.

AD: What’s Thorn?

AK: 10 years ago I saw this documentary about sex tours in Cambodia. I couldn’t believe it. It’s happening in the US and all across the world. I felt like I wasn’t being a good human if I didn’t do something about it. I formed it with my ex-wife. Started running campaigns. 72% of the transactions were happening on-line. Maybe we could use technology to make it a bad business? Now we build technology to fight the sexual exploitation of children. 5 tools (software). Help law enforcement agencies prioritise their caseload. Can use some intelligence to go through the ads and perform some analysis. We built a dashboard that law enforcement can use. Through the last year we found 18000 (?) people that were being trafficked through the US. Dark web tool to extract information from the dark web. Bunch of deterrence programs for people looking for child pornography.

AD: Are we going to continue to see technology going in this direction?

AK: It’s a function of time. As these databases become stronger, more intelligent, I think what we’re going to find is that the arduous things we do are going to become automated. We’ll become an economy of happiness. Doing things that fulfil us and make us happy, not an economy based on earning and labour. There’s going to be a real inflection point as jobs get taken over by AI. People are going to feel a real infringement of privacy. As a celebrity I fight for privacy. Rise of AI, rise of decentralised networks. People need to get smart about this stuff. Otherwise we’re going to live in a future where computers will tell us what to do, it won’t be us telling them.

AD then introduces Ray O’Farrell.

Many of you are technologists, used to technology disruption. What we’re seeing now is not just disruption, it’s a technology revolution.

  1. Mechanisation and Steam Power
  2. Mass Production
  3. Computer and Automation
  4. Cyber Physical

The initial tech was distributed, then consolidated (in large DCs or the cloud). The next step is going to be different. No longer about abstract data, now beginning to look at data generated by things of every day life. Where the physical world is meeting the digital world. Your car, your fridge, planes. We’re using data to gain insights and guide actions for positive outcomes. The physical world is not an abstract concept. Compute must be close to where the data is produced. Edge compute. The focus is also on enormous scale. Millions of mini DCs. Security is one of the most important elements, because it’s about the protection of something in the physical world.

Vast quantities of data being produced at the edge, so we’re becoming more aware of the concept of latency or, perhaps, responsiveness.

How do I build an ecosystem? Want something from end to end.

Examples: Agriculture, healthcare, and energy. Focus is on the outcome, not the technology. IoT is complex and new, and you want solutions. But you’re unique and special. Need to be able to work carefully with a broad ecosystem of partners. Making sure we can create open systems.

Ecosystem, Customers, Developers. Need to ensure that developers are empowered.

The industrial revolution has already begun. The shift to the edge requires new approaches to infrastructure (and management and operation). The world is a world of fast disruption, the pace is only going to increase. IoT used to be embedded systems. The difference is the mindset. Going to leverage the data to fundamentally change things. How is this a force for good? We’re constantly looking ahead. Not just this industrial revolution.

Ray invites John Roese to the stage to talk about AI.

Micheal talked about the person-machine era. I want to talk about the other half of the story. If we want a relationship, we need to make those machines smart. How is machine intelligence going to enter our lives?

1. This is happening now. It’s not something in the distant future.

  • Over 51% of consumers today are interacting with systems driven by AI
  • 40% reduction in Google’s DC cooling costs via DeepMind AI
  • $35B AI Chip Market by 2021

2. AI All around us

  • AI-enabled user experience
    • Careers: Natural language, visual sensing, predictive
    • Examples: digital assistant, industry robot, smart home
    • Visibility: High
  • AI-driven processes
    • Careers: Data analytics, SW development, TensorFlow / Caffe, Process Automation
    • Examples: Next Generation Customer care, Next Generation business processes, factory automation
    • Visibility: Medium
  • AI-optimized infrastructure
    • Careers: Future Design, Technical Support
    • Examples: Car Cruise Control, FAST in VMAX, Infrastructure Automation
    • Visibility: low

It easy to become infatuated with the user-focused AIs. We need to:

  • Improve the human condition – make our life easier
  • Ignite ROI
  • Scale beyond human capacity

Consequences and Benefits

  • Effort to implement
  • Job disruption
  • New job opportunities

Dell Technologies

  • Innovate on Compute – AI Engine
  • Store, Manage, Protect the Data – AI Fuel
  • Provide the multi-cloud Platform for AI – AI Brain

Group Chat Time

AD: Would you say you’re a nerd?

AK: Yes

RO: Yes

JR: Yes, super nerd

AD: We’ve seen the movies, which version do you think AI will most resemble?

JR: it’s not going to be the robot apocalypse. If we do it right, AI won’t be the story, the outcome will be. A thing that enables humanity to change.

AD: It’s a trick question

AK: It’s hard to say. There’s a lot of narrow AI that will launch in the short term various systems being replaced in specific ways. When general AI becomes more feasible – potential dramatic shift. Where the movie ends up will be different. A lot of jobs will be “pseudo-displaced” but other jobs will be created. Eg autonomous cars. “Pseudo class war”.

AD: Will your play yourself in the movie?

AK: No.

RO: I’m optimistic. The strength is when it’s a partner to the human. We’re mostly doing this in a useful way. It’s another tool in the toolbox.

AD: You hinted at the fifth industrial revolution.

RO: It’s a mindset change more than anything else. A fundamental shift in how things gets done. Like steam before it, data is powering the next revolution. I don’t think it’s going to be a sudden change.

AD: We talked about machines talking to machines.

RO: You already see a lot of machines talking to machines. Go into a modern factory. When you combine that with AI it becomes more interesting.

JR: Cloud Foundry. The next big gap in cloud native is service expression. Machines talking to machines – you need to solve the problem of common languages and APIs. That will be the biggest enabler I think.

AD: Final thoughts or comments?

JR: We are entering an era of human – machine partnership. Bringing the machines into the environment and making the machines smarter.

RO: I’m fundamentally excited about technology. When you look back over the last 10 years, it’s not stopping. The journey is continuing, there’s another transformation.

AK: All of this is really associated with the need to improve humanity. As these tools become better they’re really about improving our condition. Of all the startups I’m privileged to work on, my foundation is my greatest privilege. Solving a problem with the human condition. As we get wiser it’s important to use that knowledge and unleash it on behalf of humanity in a way that is greater than yourself. It’s far more rewarding. It just seems like it’s the right thing to do. These tools are built, and weaponised for good. And it’s all of our responsibility.

AD wraps up with a virtual mic drop.

Good session. 4 stars.