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HPC

How Texmark Chemicals pursues analysis-rich, IoT-pervasive path to the ‘refinery of the future’

How Texmark Chemicals pursues analysis-rich, IoT-pervasive path to the ‘refinery of the future’

Listen to this podcast discussion on how Texmark, with support from HPE and HPE channel partner CB Technologies, has been combining the refinery of the future approach with the best of OT, IT,  and IoT technology solutions to deliver data-driven insights that promote safety, efficiency, and unparalleled sustained operations.

How the composable approach to IT aligns automation and intelligence to overcome mounting complexity

How the composable approach to IT aligns automation and intelligence to overcome mounting complexity

Learn how higher levels of automation for data center infrastructure have evolved into truly workable solutions for composability. 

How HPC supports 'continuous integration of new ideas' for optimizing Formula 1 car design

How HPC supports 'continuous integration of new ideas' for optimizing Formula 1 car design

Learn how Alfa Romeo Racing in Switzerland leverages the latest in IT to bring hard-to-find but momentous design improvements -- from simulation to victory. 

Inside story on HPC's role in the Bridges Research Project at Pittsburgh Supercomputing Center

The next BriefingsDirect Voice of the Customer high-performance computing (HPC) success story interview examines how Pittsburgh Supercomputing Center (PSC) has developed a research computing capability, Bridges, and how that's providing new levels of analytics, insights, and efficiencies.

We'll now learn how advances in IT infrastructure and memory-driven architectures are combining to meet the new requirements for artificial intelligence (AI), big data analytics, and deep machine learning.

Inside story on HPC’s AI role in Bridges 'strategic reasoning' research at CMU

The next BriefingsDirect high performance computing (HPC) success interview examines how strategic reasoning is becoming more common and capable -- even using imperfect information.

We’ll now learn how Carnegie Mellon University and a team of researchers there are producing amazing results with strategic reasoning thanks in part to powerful new memory-intense systems architectures.

Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript ordownload a copy. 

To learn more about strategic reasoning advances, please join me in welcoming Tuomas Sandholm, Professor and Director of the Electronic Marketplaces Lab at Carnegie Mellon University in Pittsburgh. The discussion is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Tell us about strategic reasoning and why imperfect information is often the reality that these systems face?

Sandholm: In strategic reasoning we take the word “strategic” very seriously. It means game theoretic, so in multi-agent settings where you have more than one player, you can't just optimize as if you were the only actor -- because the other players are going to act strategically. What you do affects how they should play, and what they do affects how you should play.

Sandholm

Sandholm

That's what game theory is about. In artificial intelligence (AI), there has been a long history of strategic reasoning. Most AI reasoning -- not all of it, but most of it until about 12 years ago -- was really about perfect information games like Othello, Checkers, Chess and Go.

And there has been tremendous progress. But these complete information, or perfect information, games don't really model real business situations very well. Most business situations are of imperfect information.

Know what you don’t know

So you don't know the other guy's resources, their goals and so on. You then need totally different algorithms for solving these games, or game-theoretic solutions that define what rational play is, or opponent exploitation techniques where you try to find out the opponent's mistakes and learn to exploit them.

So totally different techniques are needed, and this has way more applications in reality than perfect information games have.

Gardner: In business, you don't always know the rules. All the variables are dynamic, and we don't know the rationale or the reasoning behind competitors’ actions. People sometimes are playing offense, defense, or a little of both.

Before we dig in to how is this being applied in business circumstances, explain your proof of concept involving poker. Is it Five-Card Draw?

Heads-Up No-Limit Texas Hold'em has become the leading benchmark in the AI community.

Sandholm: No, we’re working on a much harder poker game called Heads-Up No-Limit Texas Hold'em as the benchmark. This has become the leading benchmark in the AI community for testing these application-independent algorithms for reasoning under imperfect information.

The algorithms have really nothing to do with poker, but we needed a common benchmark, much like the IC chip makers have their benchmarks. We compare progress year-to-year and compare progress across the different research groups around the world. Heads-Up No-limit Texas Hold'em turned out to be great benchmark because it is a huge game of imperfect information.

It has 10 to the 161 different situations that a player can face. That is one followed by 161 zeros. And if you think about that, it’s not only more than the number of atoms in the universe, but even if, for every atom in the universe, you have a whole other universe and count all those atoms in those universes -- it will still be more than that.

Gardner: This is as close to infinity as you can probably get, right?

Sandholm: Ha-ha, basically yes.

Gardner: Okay, so you have this massively complex potential data set. How do you winnow that down, and how rapidly does the algorithmic process and platform learn? I imagine that being reactive, creating a pattern that creates better learning is an important part of it. So tell me about the learning part.

Three part harmony

Sandholm: The learning part always interests people, but it's not really the only part here -- or not even the main part. We basically have three main modules in our architecture. One computes approximations of Nash equilibrium strategies using only the rules of the game as input. In other words, game-theoretic strategies.

That doesn’t take any data as input, just the rules of the game. The second part is during play, refining that strategy. We call that subgame solving.

Then the third part is the learning part, or the self-improvement part. And there, traditionally people have done what’s called opponent modeling and opponent exploitation, where you try to model the opponent or opponents and adjust your strategies so as to take advantage of their weaknesses.

However, when we go against these absolute best human strategies, the best human players in the world, I felt that they don't have that many holes to exploit and they are experts at counter-exploiting. When you start to exploit opponents, you typically open yourself up for exploitation, and we didn't want to take that risk. In the learning part, the third part, we took a totally different approach than traditionally is taken in AI.

We are letting the opponents tell us where the holes are in our strategy. Then, in the background, using supercomputing, we are fixing those holes.

We said, “Okay, we are going to play according to our approximate game-theoretic strategies. However, if we see that the opponents have been able to find some mistakes in our strategy, then we will actually fill those mistakes and compute an even closer approximation to game-theoretic play in those spots.”

One way to think about that is that we are letting the opponents tell us where the holes are in our strategy. Then, in the background, using supercomputing, we are fixing those holes.

All three of these modules run on the Bridges supercomputer at the Pittsburgh Supercomputing Center (PSC), for which the hardware was built by Hewlett Packard Enterprise (HPE).

HPC from HPE

Overcomes Barriers

To Supercomputing and Deep Learning

Gardner: Is this being used in any business settings? It certainly seems like there's potential there for a lot of use cases. Business competition and circumstances seem to have an affinity for what you're describing in the poker use case. Where are you taking this next?

Sandholm: So far this, to my knowledge, has not been used in business. One of the reasons is that we have just reached the superhuman level in January 2017. And, of course, if you think about your strategic reasoning problems, many of them are very important, and you don't want to delegate them to AI just to save time or something like that.

Now that the AI is better at strategic reasoning than humans, that completely shifts things. I believe that in the next few years it will be a necessity to have what I call strategic augmentation. So you can't have just people doing business strategy, negotiation, strategic pricing, and product portfolio optimization.

You are going to have to have better strategic reasoning to support you, and so it becomes a kind of competition. So if your competitors have it, or even if they don't, you better have it because it’s a competitive advantage.

Gardner: So a lot of what we're seeing in AI and machine learning is to find the things that the machines do better and allow the humans to do what they can do even better than machines. Now that you have this new capability with strategic reasoning, where does that demarcation come in a business setting? Where do you think that humans will be still paramount, and where will the machines be a very powerful tool for them?

Human modeling, AI solving

Sandholm: At least in the foreseeable future, I see the demarcation as being modeling versus solving. I think that humans will continue to play a very important role in modeling their strategic situations, just to know everything that is pertinent and deciding what’s not pertinent in the model, and so forth. Then the AI is best at solving the model.

That's the demarcation, at least for the foreseeable future. In the very long run, maybe the AI itself actually can start to do the modeling part as well as it builds a better understanding of the world -- but that is far in the future.

Gardner: Looking back as to what is enabling this, clearly the software and the algorithms and finding the right benchmark, in this case the poker game are essential. But with that large of a data set potential -- probabilities set like you mentioned -- the underlying computersystems must need to keep up. Where are you in terms of the threshold that holds you back? Is this a price issue that holds you back? Is it a performance limit, the amount of time required? What are the limits, the governors to continuing?

Sandholm: It's all of the above, and we are very fortunate that we had access to Bridges; otherwise this wouldn’t have been possible at all.  We spent more than a year and needed about 25 million core hours of computing and 2.6 petabytes of data storage.

This amount is necessary to conduct serious absolute superhuman research in this field -- but it is something very hard for a professor to obtain. We were very fortunate to have that computing at our disposal.

Gardner: Let's examine the commercialization potential of this. You're not only a professor at Carnegie Mellon, you’re a founder and CEO of a few companies. Tell us about your companies and how the research is leading to business benefits.

Superhuman business strategies

Sandholm: Let’s start with Strategic Machine, a brand-new start-up company, all of two months old. It’s already profitable, and we are applying the strategic reasoning technology, which again is application independent, along with the Libratus technology, the Lengpudashi technology, and a host of other technologies that we have exclusively licensed to Strategic Machine. We are doing research and development at Strategic Machine as well, and we are taking these to any application that wants us.

HPC from HPE

Overcomes Barriers 

To Supercomputing and Deep Learning

Such applications include business strategy optimization, automated negotiation, and strategic pricing. Typically when people do pricing optimization algorithmically, they assume that either their company is a monopolist or the competitors’ prices are fixed, but obviously neither is typically true.

We are looking at how do you price strategically where you are taking into account the opponent’s strategic response in advance. So you price into the future, instead of just pricing reactively. The same can be done for product portfolio optimization along with pricing.

Let's say you're a car manufacturer and you decide what product portfolio you will offer and at what prices. Well, what you should do depends on what your competitors do and vice versa, but you don’t know that in advance. So again, it’s an imperfect-information game.

Gardner: And these are some of the most difficult problems that businesses face. They have huge billion-dollar investments that they need to line up behind for these types of decisions. Because of that pipeline, by the time they get to a dynamic environment where they can assess -- it's often too late. So having the best strategic reasoning as far in advance as possible is a huge benefit.

If you think about machine learning traditionally, it's about learning from the past. But strategic reasoning is all about figuring out what's going to happen in the future.

Sandholm: Exactly! If you think about machine learning traditionally, it's about learning from the past. But strategic reasoning is all about figuring out what's going to happen in the future. And you can marry these up, of course, where the machine learning gives the strategic reasoning technology prior beliefs, and other information to put into the model.

There are also other applications. For example, cyber security has several applications, such as zero-day vulnerabilities. You can run your custom algorithms and standard algorithms to find them, and what algorithms you should run depends on what the other opposing governments run -- so it is a game.

Similarly, once you find them, how do you play them? Do you report your vulnerabilities to Microsoft? Do you attack with them, or do you stockpile them? Again, your best strategy depends on what all the opponents do, and that's also a very strategic application.

And in upstairs blocks trading, in finance, it’s the same thing: A few players, very big, very strategic.

Gaming your own immune system

The most radical application is something that we are working on currently in the lab where we are doing medical treatment planning using these types of sequential planning techniques. We're actually testing how well one can steer a patient's T-cell population to fight cancers, autoimmune diseases, and infections better by not just using one short treatment plan -- but through sophisticated conditional treatment plans where the adversary is actually your own immune system.

Gardner: Or cancer is your opponent, and you need to beat it?

Sandholm: Yes, that’s right. There are actually two different ways to think about that, and they lead to different algorithms. We have looked at it where the actual disease is the opponent -- but here we are actually looking at how do you steer your own T-cell population.

Gardner: Going back to the technology, we've heard quite a bit from HPE about more memory-driven and edge-driven computing, where the analysis can happen closer to where the data is gathered. Are these advances of any use to you in better strategic reasoning algorithmic processing?

Algorithms at the edge

Sandholm: Yes, absolutely! We actually started running at the PSC on an earlier supercomputer, maybe 10 years ago, which was a shared-memory architecture. And then with Bridges, which is mostly a distributed system, we used distributed algorithms. As we go into the future with shared memory, we could get a lot of speedups.

We have both types of algorithms, so we know that we can run on both architectures. But obviously, the shared-memory, if it can fit our models and the dynamic state of the algorithms, is much faster.

Gardner: So the HPE Machine must be of interest to you: HPE’s advanced concept demonstration model, with a memory-driven architecture, photonics for internal communications, and so forth. Is that a technology you're keeping a keen eye on?

HPC from HPE

Overcomes Barriers 

To Supercomputing and Deep Learning

Sandholm: Yes. That would definitely be a desirable thing for us, but what we really focus on is the algorithms and the AI research. We have been very fortunate in that the PSC and HPE have been able to take care of the hardware side.

We really don’t get involved in the hardware side that much, and I'm looking at it from the outside. I'm trusting that they will continue to build the best hardware and maintain it in the best way -- so that we can focus on the AI research.

Gardner: Of course, you could help supplement the cost of the hardware by playing superhuman poker in places like Las Vegas, and perhaps doing quite well.

Sandholm: Actually here in the live game in Las Vegas they don't allow that type of computational support. On the Internet, AI has become a big problem on gaming sites, and it will become an increasing problem. We don't put our AI in there; it’s against their site rules. Also, I think it's unethical to pretend to be a human when you are not. The business opportunities, the monetary opportunities in the business applications, are much bigger than what you could hope to make in poker anyway.

Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript ordownload a copy. Sponsor: Hewlett Packard Enterprise.

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How IoT and OT collaborate to usher in the data-driven factory of the future

The next BriefingsDirect Internet of Things (IoT) technology trends interview explores how innovation is impacting modern factories and supply chains.

We’ll now learn how a leading-edge manufacturer, Hirotec, in the global automotive industry, takes advantage of IoT and Operational Technology (OT) combined to deliver dependable, managed, and continuous operations.

Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or download a copy.

Here to help us to find the best factory of the future attributes is Justin Hester, Senior Researcher in the IoT Lab at Hirotec Corp. in Hiroshima, Japan. The discussion is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: What's happening in the market with business and technology trends that’s driving this need for more modern factories and more responsive supply chains?

Hester: Our customers are demanding shorter lead times. There is a drive for even higher quality, especially in automotive manufacturing. We’re also seeing a much higher level of customization requests coming from our customers. So how can we create products that better match the unique needs of each customer?

As we look at how we can continue to compete in an ever-competitive environment, we are starting to see how the solutions from IoT can help us.

Gardner: What is it about IoT and Industrial IoT (IIoT) that allows you to do things that you could not have done before?

Hester: Within the manufacturing space, a lot of data has been there for years; for decades. Manufacturing has been very good at collecting data. The challenges we've had, though, is bringing in that data in real-time, because the amount of data is so large. How can we act on that data quicker, not on a day-by-day basis or week-by-week basis, but actually on a minute-by-minute basis, or a second-by-second basis? And how do we take that data and contextualize it?

Hester

Hester

It's one thing in a manufacturing environment to say, “Okay, this machine is having a challenge.” But it’s another thing if I can say, “This machine is having a challenge, and in the context of the factory, here's how it's affecting downstream processes, and here's what we can do to mitigate those downstream challenges that we’re going to have.” That’s where IoT starts bringing us a lot of value.

The analytics, the real-time contextualization of that data that we’ve already had in the manufacturing area, is very helpful.

Gardner: So moving from what may have been a gather, batch, analyze, report process -- we’re now taking more discrete analysis opportunities and injecting that into a wider context of efficiency and productivity. So this is a fairly big change. This is not incremental; this is a step-change advancement, right?

A huge step-change 

Hester: It’s a huge change for the market. It's a huge change for us at Hirotec. One of the things we like to talk about is what we jokingly call the Tuesday Morning Meeting. We talk about this idea that in the morning at a manufacturing facility, everyone gets together and talks about what happened yesterday, and what we can do today to make up for what happened yesterday.

Instead, now we’re making that huge step-change to say,  “Why don't we get the data to the right people with the right context and let them make a decision so they can affect what's going on, instead of waiting until tomorrow to react to what's going on?” It’s a huge step-change. We’re really looking at it as how can we take small steps right away to get to that larger goal.

In manufacturing areas, there's been a lot of delay, confusion, and hesitancy to move forward because everyone sees the value, but it's this huge change, this huge project. At Hirotec, we’re taking more of a scaled approach, and saying let's start small, let’s scale up, let’s learn along the way, let's bring value back to the organization -- and that's helped us move very quickly.

Gardner: We’d like to hear more about that success story but in the meantime, tell us about Hirotec for those who don't know of it. What role do you play in the automotive industry, and how are you succeeding in your markets?

Hester: Hirotec is a large, tier-1 automotive supplier. What that means is we supply parts and systems directly to the automotive original equipment manufacturers (OEMs), like Mazda, General Motors, FCA, Ford, and we specialize in door manufacturing, as well as exhaust system manufacturing. So every year we make about 8 million doors, 1.8 million exhaust systems, and we provide those systems mainly to Mazda and General Motors, but also we provide that expertise through tooling.

For example, if an automotive OEM would like Hirotec’s expertise in producing these parts, but they would like to produce them in-house, Hirotec has a tooling arm where we can provide that tooling for automotive manufacturing. It's an interesting strategy that allows us to take advantage of data both in our facilities, but then also work with our customers on the tooling side to provide those lessons learned and bring them value there as well.

Gardner: How big of a distribution are we talking about? How many factories, how many countries; what’s the scale here?

Hester: We are based in Hiroshima, Japan, but we’re actually in nine countries around the world, currently with 27 facilities. We have reached into all the major continents with automotive manufacturing: we’re in North America, we’re in Europe, we’re all throughout Asia, in China and India. We have a large global presence. Anywhere you find automotive manufacturing, we’re there supporting it.

Discover How the

IoT Advantage

Works in Multiple Industries

Gardner: With that massive scale, very small improvements can turn into very big benefits. Tell us why the opportunity in a manufacturing environment to eke out efficiency and productivity has such big payoffs.

Hester: So especially in manufacturing, what we find when we get to those large scales like you're alluding to is that a 1 percent or 2 percent improvement has huge financial benefits. And so the other thing is in manufacturing, especially automotive manufacturing, we tend to standardize our processes, and within Hirotec, we’ve done a great job of standardizing that world-class leadership in door manufacturing.

And so what we find is when we get improvements not only in IoT but anywhere in manufacturing, if we can get 1 percent or 2 percent, not only is that a huge financial benefit but because we standardized globally, we can move that to our other facilities very quickly, doubling down on that benefit.

Gardner: Well, clearly Hirotec sees this as something to really invest in, they’ve created the IoT Lab. Tell me a little bit about that and how that fits into this?

The IoT Lab works

Hester: The IoT Lab is a very exciting new group, it's part of our Advanced Engineering Center (AEC). The AEC is a group out of our global headquarters and this group is tasked with the five- to 10-year horizon. So they're able to work across all of our global organizations with tooling, with engineering, with production, with sales, and even our global operations groups. Our IoT group goes and finds solutions that can bring value anywhere in the organization through bringing in new technologies, new ideas, and new solutions.

And so we formed the IoT Lab to find how can we bring IoT-based solutions into the manufacturing space, into the tooling space, and how actually can those solutions not only help our manufacturing and tooling teams but also help our IT teams, our finance teams, and our sales teams.

Gardner: Let's dig back down a little bit into why IT, IoT and Operational Technology (OT) are into this step-change opportunity, looking for some significant benefits but being careful in how to institute that. What is required when you move to a more an IT-focused, a standard-platform approach -- across all the different systems -- that allows you to eke these great benefits?

Tell us about how IoT as a concept is working its way into the very edge of the factory floor.

Discover How the

IoT Advantage

Works in Multiple Industries

Hester: One of the things we’re seeing is that IT is beginning to meld, like you alluded to, with OT -- and there really isn't a distinction between OT and IT anymore. What we're finding is that we’re starting to get to these solution levels by working with partners such as PTC and Hewlett Packard Enterprise (HPE) to bring our IT group and our OT group all together within Hirotec and bring value to the organization.

What we find is there is no longer a need in OT that becomes a request for IT to support it, and also that IT has a need and so they go to OT for support. What we are finding is we have organizational needs, and we’re coming to the table together to make these changes. And that actually within itself is bringing even more value to the organization.

Instead of coming last-minute to the IT group and saying, “Hey, we need your support for all these different solutions, and we’ve already got everything set, and you are just here to put it in,” what we are seeing, is that they bring the expertise in, help us out upfront, and we’re finding better solutions because we are getting experts both from OT and IT together.

We are seeing this convergence of these two teams working on solutions to bring value. And they're really moving everything to the edge. So where everyone talks about cloud-based computing -- or maybe it’s in their data center -- where we are finding value is in bringing all of these solutions right out to the production line.

We are doing data collection right there, but we are also starting to do data analytics right at the production line level, where it can bring the best value in the fastest way.

Gardner: So it’s an auspicious time because just as you are seeking to do this, the providers of technology are creating micro data centers, and they are creating Edgeline converged systems, and they are looking at energy conservation so that they can do this in an affordable way -- and with storage models that can support this at a competitive price.

What is it about the way that IT is evolving and providing platforms and systems that has gotten you and The IoT Lab so excited?

Excitement at the edge  

Hester: With IoT and IT platforms, originally to do the analytics, we had to go up to the cloud -- that was the only place where the compute power existed. Solution providers now are bringing that level of intelligence down to the edge. We’re hearing some exciting things from HPE on memory-driven computing, and that's huge for us because as we start doing these very complex analytics at the edge, we need that power, that horsepower, to run different applications at the same time at the production line. And something like memory-driven solutions helps us accomplish that.

It's one thing to have higher-performance computing, but another thing to gain edge computing that's proper for the factory environment. In a manufacturing environment it's not conducive to a standard servers, a standard rack where it needs dust protection and heat protection -- that doesn't exist in a manufacturing environment.

The other thing we're beginning to see with edge computing, that HPE provides with Edgeline products, is that we have computers that have high power, high ability to perform the analytics and data collection capabilities -- but they're also proper for the environment.

I don't need to build out a special protection unit with special temperature control, humidity control – all of which drives up energy costs, which drives up total costs. Instead, we’re able to run edge computing in the environment as it should be on its own, protected from what comes in a manufacturing environment -- and that's huge for us.

Gardner: They are engineering these systems now with such ruggedized micro facilities in mind. It's quite impressive that the very best of what a data center can do, can now be brought to the very worst types of environments. I'm sure we'll see more of that, and I am sure we'll see it get even smaller and more powerful.

Do you have any examples of where you have already been able to take IoT in the confluence of OT and IT to a point where you can demonstrate entirely new types of benefits? I know this is still early in the game, but it helps to demonstrate what you can do in terms of efficiency, productivity, and analytics. What are you getting when you do this well?

IoT insights save time and money

Hester: Taking the stepped strategy that we have, we actually started at Hirotec very small with only eight machines in North America and we were just looking to see if the machines are on, are they running, and even from there, we saw a value because all of a sudden we were getting that real-time contextualized insight into the whole facility. We then quickly moved over to one of our production facilities in Japan, where we have a brand-new robotic inspection system, and this system uses vision sensors, laser sensors, force sensors -- and it's actually inspecting exhaust systems before they leave the facility.

We very quickly implemented an IoT solution in that area, and all we did was we said, “Hey, we just want to get insight into the data, so we want to be able to see all these data points. Over 400 data points are created every inspection. We want to be able to see this data, compared in historical ways -- so let’s bring context to that data, and we want to provide it in real-time.”

Discover How the

IoT Advantage

Works in Multiple Industries

What we found from just those two projects very quickly is that we're bringing value to the organization because now our teams can go in and say, “Okay, the system is doing its job, it's inspecting things before they leave our facility to make sure our customers always get a high-quality product.” But now, we’re able to dive in and find different trends that we weren't able to see before because all we were doing is saying, “Okay, this system leaves the facility or this system doesn't.”

And so already just from that application, we’ve been able to find ways that our engineers can even increase the throughput and the reliability of the system because now they have these historical trends. They were able to do a root-cause analysis on some improvements that would have taken months of investigation; it was completed in less than a week for us.

And so that's a huge value -- not only in that my project costs go down but now I am able to impact the organization quicker, and that's the big thing that Hirotec is seeing. It’s one thing to talk about the financial cost of a project, or I can say, “Okay, here is the financial impact,” but what we are seeing is that we’re moving quicker.

And so, we're having long-term financial benefits because we’re able to react to things much faster. In this case, we’re able to reduce months of investigation down to a week. That means that when I implement my solution quicker, I'm now bringing that impact to the organization even faster, which has long-term benefits. We are already seeing those benefits today.

Gardner: You’ll obviously be able to improve quality, you’ll be able to reduce the time to improving that quality, gain predictive analytics in your operations, but also it sounds like you are going to gain metadata insights that you can take back into design for the next iteration of not only the design for the parts but the design for the tooling as well and even the operations around that. So that intelligence at the edge can be something that is a full lifecycle process, it goes right back to the very initiation of both the design and the tooling.

Data-driven design, decisions 

Hester: Absolutely, and so, these solutions, they can't live in a silo. We're really starting to look at these ideas of what some people call the Digital Thread, the Digital Twin. We’re starting to understand what does that mean as you loop this data back to our engineering teams -- what kind of benefits can we see, how can we improve our processes, how can we drive out into the organization?

And one of the biggest things with IoT-based solutions is that they can't stay inside this box, where we talked about OT to IT, we are talking about manufacturing, engineering, these IoT solutions at their best, all they really do is bring these groups together and bring a whole organization together with more contextualized data to make better decisions faster.

And so, exactly to your point, as we are looping back, we’re able to start understanding the benefit we’re going to be seeing from bringing these teams together.

Gardner: One last point before we close out. It seems to me as well that at a macro level, this type of data insight and efficiency can be brought into the entire supply chain. As you're providing certain elements of an automobile, other suppliers are providing what they specialize in, too, and having that quality control and integration and reduced time-to-value or mean-time-to-resolution of the production issues, and so forth, can be applied at a macro level.

So how does the automotive supplier itself look at this when it can take into consideration all of its suppliers like Hirotec are doing?

Start small 

Hester: It's a very early phase, so a lot of the suppliers are starting to understand what this means for them. There is definitely a macro benefit that the industry is going to see in five to 10 years. Suppliers now need to start small. One of my favorite pictures is a picture of the ocean and a guy holding a lighter. It [boiling the ocean] is not going to happen. So we see these huge macro benefits of where we’re going, but we have to start out somewhere.

Discover How the

IoT Advantage

Works in Multiple Industries

A lot of suppliers, what we’re recommending to them, is to do the same thing we did, just start small with a couple of machines, start getting that data visualized, start pulling that data into the organization. Once you do that, you start benefiting from the data, and then start finding new use-cases.

As these suppliers all start doing their own small projects and working together, I think that's when we are going to start to see the macro benefits but in about five to 10 years out in the industry.

Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or download a copy. Sponsor: Hewlett Packard Enterprise.

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DreamWorks Animation crafts its next era of dynamic IT infrastructure

The next BriefingsDirect Voice of the Customer thought leader interview examines how DreamWorks Animation is building a multipurpose, all-inclusive, and agile data center capability.

Learn here why a new era of responsive and dynamic IT infrastructure is demanded, and how one high-performance digital manufacturing leader aims to get there sooner rather than later. 

Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or download a copy.

Here to describe how an entertainment industry innovator leads the charge for bleeding-edge IT-as-a-service capabilities is Jeff Wike, CTO of DreamWorks Animation in Glendale, California. The discussion is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Tell us why the older way of doing IT infrastructure and hosting apps and data just doesn't cut it anymore. What has made that run out of gas?

Wike: You have to continue to improve things. We are in a world where technology is advancing at an unbelievable pace. The amount of data, the capability of the hardware, the intelligence of the infrastructure are coming. In order for any business to stay ahead of the curve -- to really drive value into the business – it has to continue to innovate.

Gardner: IT has become more pervasive in what we do. I have heard you all refer to yourselves as digital manufacturing. Are the demands of your industry also a factor in making it difficult for IT to keep up?

Wike: When I say we are a digital manufacturer, it’s because we are a place that manufacturers content, whether it's animated films or TV shows; that content is all made on the computer. An artist sits in front of a workstation or a monitor, and is basically building these digital assets that we put through simulations and rendering so in the end it comes together to produce a movie.

Wike

Wike

That's all about manufacturing, and we actually have a pipeline, but it's really like an assembly line. I was looking at a slide today about Henry Ford coming up with the first assembly line; it's exactly what we are doing, except instead of adding a car part, we are adding a character, we’re adding a hair to a character, we’re adding clothes, we’re adding an environment, and we’re putting things into that environment.

We are manufacturing that image, that story, in a linear way, but also in an iterative way. We are constantly adding more details as we embark on that process of three to four years to create one animated film.

Gardner: Well, it also seems that we are now taking that analogy of the manufacturing assembly line to a higher plane, because you want to have an assembly line that doesn't just make cars -- it can make cars and trains and submarines and helicopters, but you don't have to change the assembly line, you have to adjust and you have to utilize it properly.

So it seems to me that we are at perhaps a cusp in IT where the agility of the infrastructure and its responsiveness to your workloads and demands is better than ever.

Greater creativity, increased efficiency

Wike: That's true. If you think about this animation process or any digital manufacturing process, one issue that you have to account for is legacy workflows, legacy software, and legacy data formats -- all these things are inhibitors to innovation. There are a lot of tools. We actually write our own software, and we’re very involved in projects related to computer science at the studio.

We’ll ask ourselves, “How do you innovate? How can you change your environment to be able to move forward and innovate and still carry around some of those legacy systems?”

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And one of the things we’ve done over the past couple of years is start to re-architect all of our software tools in order to take advantage of massive multi-core processing to try to give artists interactivity into their creative process. It’s about iterations. How many things can I show a director, how quickly can I create the scene to get it approved so that I can hand it off to the next person, because there's two things that you get out of that.

One, you can explore more and you can add more creativity. Two, you can drive efficiency, because it's all about how much time, how many people are working on a particular project and how long does it take, all of which drives up the costs. So you now have these choices where you can add more creativity or -- because of the compute infrastructure -- you can drive efficiency into the operation.

So where does the infrastructure fit into that, because we talk about tools and the ability to make those tools quicker, faster, more real-time? We conducted a project where we tried to create a middleware layer between running applications and the hardware, so that we can start to do data abstraction. We can get more mobile as to where the data is, where the processing is, and what the systems underneath it all are. Until we could separate the applications through that layer, we weren’t really able to do anything down at the core.

Core flexibility, fast

Now that we have done that, we are attacking the core. When we look at our ability to replace that with new compute, and add the new templates with all the security in it -- we want that in our infrastructure. We want to be able to change how we are using that infrastructure -- examine usage patterns, the workflows -- and be able to optimize.

Before, if we wanted to do a new project, we’d say, “Well, we know that this project takes x amount of infrastructure. So if we want to add a project, we need 2x,” and that makes a lot of sense. So we would build to peak. If at some point in the last six months of a show, we are going to need 30,000 cores to be able to finish it in six months, we say, “Well, we better have 30,000 cores available, even though there might be times when we are only using 12,000 cores.” So we were buying to peak, and that’s wasteful.

What we wanted was to be able to take advantage of those valleys, if you will, as an opportunity -- the opportunity to do other types of projects. But because our infrastructure was so homogeneous, we really didn't have the ability to do a different type of project. We could create another movie if it was very much the same as a previous film from an infrastructure-usage standpoint.

By now having composable, or software-defined infrastructure, and being able to understand what the requirements are for those particular projects, we can recompose our infrastructure -- parts of it or all of it -- and we can vary that. We can horizontally scale and redefine it to get maximum use of our infrastructure -- and do it quickly.

Gardner: It sounds like you have an assembly line that’s very agile, able to do different things without ripping and replacing the whole thing. It also sounds like you gain infrastructure agility to allow your business leaders to make decisions such as bringing in new types of businesses. And in IT, you will be responsive, able to put in the apps, manage those peaks and troughs.

Does having that agility not only give you the ability to make more and better movies with higher utilization, but also gives perhaps more wings to your leaders to go and find the right business models for the future?

Wike: That’s absolutely true. We certainly don't want to ever have a reason to turn down some exciting project because our digital infrastructure can’t support it. I would feel really bad if that were the case.

In fact, that was the case at one time, way back when we produced Spirit: Stallion of the Cimarron. Because it was such a big movie from a consumer products standpoint, we were asked to make another movie for direct-to-video. But we couldn't do it; we just didn’t have the capacity, so we had to just say, “No.” We turned away a project because we weren’t capable of doing it. The time it would take us to spin up a project like that would have been six months.

The world is great for us today, because people want content -- they want to consume it on their phone, on their laptop, on the side of buildings and in theaters. People are looking for more content everywhere.

Yet projects for varied content platforms require different amounts of compute and infrastructure, so we want to be able to create content quickly and avoid building to peak, which is too expensive. We want to be able to be flexible with infrastructure in order to take advantage of those opportunities.

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Gardner: How is the agility in your infrastructure helping you reach the right creative balance? I suppose it’s similar to what we did 30 years ago with simultaneous engineering, where we would design a physical product for manufacturing, knowing that if it didn't work on the factory floor, then what's the point of the design? Are we doing that with digital manufacturing now?

Artifact analytics improve usage, rendering

Wike: It’s interesting that you mention that. We always look at budgets, and budgets can be money budgets, it can be rendering budgets, it can be storage budgets, and networking -- I mean all of those things are commodities that are required to create a project.

Artists, managers, production managers, directors, and producers are all really good at managing those projects if they understand what the commodity is. Years ago we used to complain about disk space: “You guys are using too much disk space.” And our production department would say, “Well, give me a tool to help me manage my disk space, and then I can clean it up. Don’t just tell me it's too much.”

One of the initiatives that we have incorporated in recent years is in the area of data analytics. We re-architected our software and we decided we would re-instrument everything. So we started collecting artifacts about rendering and usage. Every night we ran every digital asset that had been created through our rendering, and we also collected analytics about it. We now collect 1.2 billion artifacts a night.

And we correlate that information to a specific asset, such as a character, basket, or chair -- whatever it is that I am rendering -- as well as where it’s located, which shot it’s in, which sequence it’s in, and which characters are connected to it. So, when an artist wants to render a particular shot, we know what digital resources are required to be able to do that.

One of the things that’s wasteful of digital resources is either having a job that doesn't fit the allocation that you assign to it, or not knowing when a job is complete. Some of these rendering jobs and simulations will take hours and hours -- it could take 10 hours to run.

At what point is it stuck? At what point do you kill that job and restart it because something got wedged and it was a dependency? And you don't really know, you are just watching it run. Do I pull the plug now? Is it two minutes away from finishing, or is it never going to finish?

Just the facts

Before, an artist would go in every night and conduct a test render. And they would say, “I think this is going to take this much memory, and I think it's going to take this long.” And then we would add a margin of error, because people are not great judges, as opposed to a computer. This is where we talk about going from feeling to facts.

So now we don't have artists do that anymore, because we are collecting all that information every night. We have machine learning that then goes in and determines requirements. Even though a certain shot has never been run before, it is very similar to another previous shot, and so we can predict what it is going to need to run.

Now, if a job is stuck, we can kill it with confidence. By doing that machine learning and taking the guesswork out of the allocation of resources, we were able to save 15 percent of our render time, which is huge.

I recently listened to a gentleman talk about what a difference of 1 percent improvement would be. So 15 percent is huge, that's 15 percent less money you have to spend. It's 15 percent faster time for a director to be able to see something. It's 15 percent more iterations. So that was really huge for us.

Gardner: It sounds like you are in the digital manufacturing equivalent of working smarter and not harder. With more intelligence, you can free up the art, because you have nailed the science when it comes to creating something.

Creative intelligence at the edge

Wike: It's interesting; we talk about intelligence at the edge and the Internet of Things (IoT), and that sort of thing. In my world, the edge is actually an artist. If we can take intelligence about their work, the computational requirements that they have, and if we can push that data -- that intelligence -- to an artist, then they are actually really, really good at managing their own work.

It's only a problem when they don't have any idea that six months from now it's going to cause a huge increase in memory usage or render time. When they don't know that, it's hard for them to be able to self-manage. But now we have artists who can access Tableau reports everyday and see exactly what the memory usage was or the compute usage of any of the assets they’ve created, and they can correct it immediately.

On Megamind, a film DreamWorks Animation released several years ago, it was prior to having the data analytics in place, and the studio encountered massive rendering spikes on certain shots. We really didn't understand why.

After the movie was complete, when we could go back and get printouts of logs to analyze, we determined that these peaks in rendering resources were caused by his watch. Whenever the main character’s watch was in a frame, the render times went up. We looked at the models, and well-intended artists had taken a model of a watch and every gear was modeled, and it was just a huge, heavy asset to render.

But it was too late to do anything about it. But now, if an artist were to create that watch today, they would quickly find out that they had really over-modeled that watch. We would then need to go in and reduce that asset down, because it's really not a key element to the story. And they can do that today, which is really great.

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Gardner: I am a big fan of animated films, and I am so happy that my kids take me to see them because I enjoy them as much as they do. When you mention an artist at the edge, it seems to me it’s more like an army at the edge, because I wait through the end of the movie, and I look at the credits scroll -- hundreds and hundreds of people at work putting this together.

So you are dealing with not just one artist making a decision, you have an army of people. It's astounding that you can bring this level of data-driven efficiency to it.

Movie-making’s mobile workforce

Wike: It becomes so much more important, too, as we become a more mobile workforce. 

Now it becomes imperative to be able to obtain the information about what those artists are doing so that they can collaborate. We know what value we are really getting from that, and so much information is available now. If you capture it, you can find so many things that we can really understand better about our creative process to be able to drive efficiency and value into the entire business.

Gardner: Before we close out, maybe a look into the crystal ball. With things like auto-scaling and composable infrastructure, where do we go next with computing infrastructure? As you say, it's now all these great screens in people's hands, handling high-definition, all the networks are able to deliver that, clearly almost an unlimited opportunity to bring entertainment to people. What can you now do with the flexible, efficient, optimized infrastructure? What should we expect?

Wike: There's an explosion in content and explosion in delivery platforms. We are exploring all kinds of different mediums. I mean, there’s really no limit to where and how one can create great imagery. The ability to do that, the ability to not say “No” to any project that comes along is going to be a great asset.

We always say that we don't know in the future how audiences are going to consume our content. We just know that we want to be able to supply that content and ensure that it’s the highest quality that we can deliver to audiences worldwide.

Gardner: It sounds like you feel confident that the infrastructure you have in place is going to be able to accommodate whatever those demands are. The art and the economics are the variables, but the infrastructure is not.

Wike: Having a software-defined environment is essential. I came from the software side; I started as a programmer, so I am coming back into my element. I really believe that now that you can compose infrastructure, you can change things with software without having to have people go in and rewire or re-stack, but instead change on-demand. And with machine learning, we’re able to learn what those demands are.

I want the computers to actually optimize and compose themselves so that I can rest knowing that my infrastructure is changing, scaling, and flexing in order to meet the demands of whatever we throw at it.

Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or download a copy. Sponsor: Hewlett Packard Enterprise.

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How Imagine Communications leverages edge computing and HPC for live multiscreen IP video

The next BriefingsDirect Voice of the Customer HPC and edge computing strategies interview explores how a video delivery and customization capability has moved to the network edge -- and closer to consumers -- to support live, multi-screen Internet Protocol (IP) entertainment delivery. 

Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or download a copy.

We’ll learn how hybrid technology and new workflows for IP-delivered digital video are being re-architected -- with significant benefits to the end-user experience, as well as with new monetization values to the content providers.

Our guest is Glodina Connan-Lostanlen, Chief Marketing Officer at Imagine Communications in Frisco, Texas. The discussion is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Your organization has many major media clients. What are the pressures they are facing as they look to the new world of multi-screen video and media?

Connan-Lostanlen: The number-one concern of the media and entertainment industry is the fragmentation of their audience. We live with a model supported by advertising and subscriptions that rely primarily on linear programming, with people watching TV at home.

Connan-Lostanlen

Connan-Lostanlen

And guess what? Now they are watching it on the go -- on their telephones, on their iPads, on their laptops, anywhere. So they have to find the way to capture that audience, justify the value of that audience to their advertisers, and deliver video content that is relevant to them. And that means meeting consumer demand for several types of content, delivered at the very time that people want to consume it.  So it brings a whole range of technology and business challenges that our media and entertainment customers have to overcome. But addressing these challenges with new technology that increases agility and velocity to market also creates opportunities.

For example, they can now try new content. That means they can try new programs, new channels, and they don’t have to keep them forever if they don’t work. The new models create opportunities to be more creative, to focus on what they are good at, which is creating valuable content. At the same time, they have to make sure that they cater to all these different audiences that are either static or on the go.

Gardner: The media industry has faced so much change over the past 20 years, but this is a major, perhaps once-in-a-generation, level of change -- when you go to fully digital, IP-delivered content.

As you say, the audience is pulling the providers to multi-screen support, but there is also the capability now -- with the new technology on the back-end -- to have much more of a relationship with the customer, a one-to-one relationship and even customization, rather than one-to-many. Tell us about the drivers on the personalization level.

Connan-Lostanlen: That’s another big upside of the fragmentation, and the advent of IP technology -- all the way from content creation to making a program and distributing it. It gives the content creators access to the unique viewers, and the ability to really engage with them -- knowing what they like -- and then to potentially target advertising to them. The technology is there. The challenge remains about how to justify the business model, how to value the targeted advertising; there are different opinions on this, and there is also the unknown or the willingness of several generations of viewers to accept good advertising.

That is a great topic right now, and very relevant when we talk about linear advertising and dynamic ad insertion (DAI). Now we are able to -- at the very edge of the signal distribution, the video signal distribution -- insert an ad that is relevant to each viewer, because you know their preferences, you know who they are, and you know what they are watching, and so you can determine that an ad is going to be relevant to them.

But that means media and entertainment customers have to revisit the whole infrastructure. It’s not necessary rebuilding, they can put in add-ons. They don’t have to throw away what they had, but they can maintain the legacy infrastructure and add on top of it the IP-enabled infrastructure to let them take advantage of these capabilities.

Gardner: This change has happened from the web now all the way to multi-screen. With the web there was a model where you would use a content delivery network (CDN) to take the object, the media object, and place it as close to the edge as you could. What’s changed and why doesn’t that model work as well?

Connan-Lostanlen: I don’t know yet if I want to say that model doesn’t work anymore. Let’s let the CDN providers enhance their technology. But for sure, the volume of videos that we are consuming everyday is exponentially growing. That definitely creates pressure in the pipe. Our role at the front-end and the back-end is to make sure that videos are being created in different formats, with different ads, and everything else, in the most effective way so that it doesn’t put an undue strain on the pipe that is distributing the videos.

We are being pushed to innovate further on the type of workflows that we are implementing at our customers’ sites today, to make it efficient, to not leave storage at the edge and not centrally, and to do transcoding just-in-time. These are the things that are being worked on. It’s a balance between available capacity and the number of programs that you want to send across to your viewers – and how big your target market is.

The task for us on the back-end is to rethink the workflows in a much more efficient way. So, for example, this is what we call the digital-first approach, or unified distribution. Instead of planning a linear channel that goes the traditional way and then adding another infrastructure for multi-screen, on all those different platforms and then cable, and satellite, and IPTV, etc. -- why not design the whole workflow digital-first. This frees the content distributor or provider to hold off on committing to specific platforms until the video has reached the edge. And it’s there that the end-user requirements determine how they get the signal.

This is where we are going -- to see the efficiencies happen and so remove the pressure on the CDNs and other distribution mechanisms, like over-the-air.

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Gardner: It means an intelligent edge capability, whereas we had an intelligent core up until now. We’ll also seek a hybrid capability between them, growing more sophisticated over time.

We have a whole new generation of technology for video delivery. Tell us about Imagine Communications. How do you go to market? How do you help your customers?

Education for future generations

Connan-Lostanlen: Two months ago we were in Las Vegas for our biggest tradeshow of the year, the NAB Show. At the event, our customers first wanted to understand what it takes to move to IP -- so the “how.” They understand the need to move to IP, to take advantage of the benefits that it brings. But how do they do this, while they are still navigating the traditional world?

It’s not only the “how,” it’s needing examples of best practices. So we instructed them in a panel discussion, for example, on Over the Top Technology (OTT), which is another way of saying IP-delivered, and what it takes to create a successful multi-screen service. Part of the panel explained what OTT is, so there’s a lot of education.

There is also another level of education that we have to provide, which is moving from the traditional world of serial digital interfaces (SDIs) in the broadcast industry to IP. It’s basically saying analog video signals can be moved into digital. Then not only is there a digitally sharp signal, it’s an IP stream. The whole knowledge about how to handle IP is new to our own industry, to our own engineers, to our own customers. We also have to educate on what it takes to do this properly.

One of the key things in the media and entertainment industry is that there’s a little bit of fear about IP, because no one really believed that IP could handle live signals. And you know how important live television is in this industry – real-time sports and news -- this is where the money comes from. That’s why the most expensive ads are run during the Super Bowl.

It’s essential to be able to do live with IP – it’s critical. That’s why we are sharing with our customers the real-life implementations that we are doing today.

We are also pushing multiple standards forward. We work with our competitors on these standards. We have set up a trade association to accelerate the standards work. We did all of that. And as we do this, it forces us to innovate in partnership with customers and bring them on board. They are part of that trade association, they are part of the proof-of-concept trials, and they are gladly sharing their experiences with others so that the transition can be accelerated.

Gardner: Imagine Communications is then a technology and solutions provider to the media content companies, and you provide the means to do this. You are also doing a lot with ad insertion, billing, in understanding more about the end-user and allowing that data flow from the edge back to the core, and then back to the edge to happen.

At the heart of it all

Connan-Lostanlen: We do everything that happens behind the camera -- from content creation all the way to making a program and distributing it. And also, to your point, on monetizing all that with a management system. We have a long history of powering all the key customers in the world for their advertising system. It’s basically an automated system that allows the selling of advertising spots, and then to bill them -- and this is the engine of where our customers make money. So we are at the heart of this.

We are in the prime position to help them take advantage of the new advertising solutions that exist today, including dynamic ad insertion. In other words, how you target ads to the single viewer. And the challenge for them is now that they have a campaign, how do they design it to cater both to the linear traditional advertising system as well as the multi-screen or web mobile application? That's what we are working on. We have a whole set of next-generation platforms that allow them to take advantage of both in a more effective manner.

Gardner: The technology is there, you are a solutions provider. You need to find the best ways of storing and crunching data, close to the edge, and optimizing networks. Tell us why you choose certain partners and what are the some of the major concerns you have when you go to the technology marketplace?

Connan-Lostanlen: One fundamental driver here, as we drive the transition to IP in this industry, is in being able to rely on consumer-off-the-shelf (COTS) platforms. But even so, not all COTS platforms are born equal, right?

For compute, for storage, for networking, you need to rely on top-scale hardware platforms, and that’s why about two years ago we started to work very closely with Hewlett Packard Enterprise (HPE) for both our compute and storage technology.

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We develop the software appliances that run on those platforms, and we sell this as a package with HPE. It’s been a key value proposition of ours as we began this journey to move to IP. We can say, by the way, our solutions run on HPE hardware. That's very important because having high-performance compute (HPC) that scales is critical to the broadcast and media industry. Having storage that is highly reliable is fundamental because going off the air is not acceptable. So it's 99.9999 percent reliable, and that’s what we want, right?

It’s a fundamental part of our message to our customers to say, “In your network, put Imagine solutions, which are powered by one of the top compute and storage technologies.”

Gardner: Another part of the change in the marketplace is this move to the edge. It’s auspicious that just as you need to have more storage and compute efficiency at the edge of the network, close to the consumer, the infrastructure providers are also designing new hardware and solutions to do just that. That's also for the Internet of Things (IoT) requirements, and there are other drivers. Nonetheless, it's an industry standard approach.

What is it about HPE Edgeline, for example, and the architecture that HPE is using, that makes that edge more powerful for your requirements? How do you view this architectural shift from core data center to the edge?

Optimize the global edge

Connan-Lostanlen: It's a big deal because we are going to be in a hybrid world. Most of our customers, when they hear about cloud, we have to explain it to them. We explain that they can have their private cloud where they can run virtualized applications on-premises, or they can take advantage of public clouds.

Being able to have a hybrid model of deployment for their applications is critical, especially for large customers who have operations in several places around the globe. For example, such big names as Disney, Turner –- they have operations everywhere. For them, being able to optimize at the edge means that you have to create an architecture that is geographically distributed -- but is highly efficient where they have those operations. This type of technology helps us deliver more value to the key customers.

Gardner: The other part of that intelligent edge technology is that it has the ability to be adaptive and customized. Each region has its own networks, its own regulation, and its own compliance, security, and privacy issues. When you can be programmatic as to how you design your edge infrastructure, then a custom-applications-orientation becomes possible.

Is there something about the edge architecture that you would like to see more of? Where do you see this going in terms of the capabilities of customization added-on to your services?

Connan-Lostanlen: One of the typical use-cases that we see for those big customers who have distributed operations is that they like to try and run their disaster recovery (DR) site in a more cost-effective manner. So the flexibility that an edge architecture provides to them is that they don’t have to rely on central operations running DR for everybody. They can do it on their own, and they can do it cost-effectively. They don't have to recreate the entire infrastructure, and so they do DR at the edge as well.

We especially see this a lot in the process of putting the pieces of the program together, what we call “play out,” before it's distributed. When you create a TV channel, if you will, it’s important to have end-to-end redundancy -- and DR is a key driver for this type of application.

Gardner: Are there some examples of your cutting-edge clients that have adopted these solutions? What are the outcomes? What are they able to do with it?

Pop-up power

Connan-Lostanlen: Well, it’s always sensitive to name those big brand names. They are very protective of their brands. However, one of the top ones in the world of media and entertainment has decided to move all of their operations -- from content creation, planning, and distribution -- to their own cloud, to their own data center.

They are at the forefront of playing live and recorded material on TV -- all from their cloud. They needed strong partners in data centers. So obviously we work with them closely, and the reason why they do this is simply to really take advantage of the flexibility. They don't want to be tied to a restricted channel count; they want to try new things. They want to try pop-up channels. For the Oscars, for example, it’s one night. Are you going to recreate the whole infrastructure if you can just check it on and off, if you will, out of their data center capacity? So that's the key application, the pop-up channels and ability to easily try new programs.

Gardner: It sounds like they are thinking of themselves as an IT company, rather than a media and entertainment company that consumes IT. Is that shift happening?

Connan-Lostanlen: Oh yes, that's an interesting topic, because I think you cannot really do this successfully if you don’t start to think IT a little bit. What we are seeing, interestingly, is that our customers typically used to have the IT department on one side, the broadcast engineers on the other side -- these were two groups that didn't speak the same language. Now they get together, and they have to, because they have to design together the solution that will make them more successful. We are seeing this happening.

I wouldn't say yet that they are IT companies. The core strength is content, that is their brand, that's what they are good at -- creating amazing content and making it available to as many people as possible.

They have to understand IT, but they can't lose concentration on their core business. I think the IT providers still have a very strong play there. It's always happening that way.

In addition to disaster recovery being a key application, multi-screen delivery is taking advantage of that technology, for sure.

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Gardner: These companies are making this cultural shift to being much more technically oriented. They think about standard processes across all of what they do, and they have their own core data center that's dynamic, flexible, agile and cost-efficient. What does that get for them? Is it too soon, or do we have some metrics of success for companies that make this move toward a full digitally transformed organization?

Connan-Lostanlen: They are very protective about the math. It is fair to say that the up-front investments may be higher, but when you do the math over time, you do the total cost of ownership for the next 5 to 10 years -- because that’s typically the life cycle of those infrastructures – then definitely they do save money. On the operational expenditure (OPEX) side [of private cloud economics] it’s much more efficient, but they also have upside on additional revenue. So net-net, the return on investment (ROI) is much better. But it’s kind of hard to say now because we are still in the early days, but it’s bound to be a much greater ROI.

Another specific DR example is in the Middle East. We have a customer there who decided to operate the DR and IP in the cloud, instead of having a replicated system with satellite links in between. They were able to save $2 million worth of satellite links, and that data center investment, trust me, was not that high. So it shows that the ROI is there.

My satellite customers might say, “Well, what are you trying to do?” The good news is that they are looking at us to help them transform their businesses, too. So big satellite providers are thinking broadly about how this world of IP is changing their game. They are examining what they need to do differently. I think it’s going to create even more opportunities to reduce costs for all of our customers.

IT enters a hybrid world

Gardner: That's one of the intrinsic values of a hybrid IT approach -- you can use many different ways to do something, and then optimize which of those methods works best, and also alternate between them for best economics. That’s a very powerful concept.

Connan-Lostanlen: The world will be a hybrid IT world, and we will take advantage of that. But, of course, that will come with some challenges. What I think is next is the number-one question that I get asked.

Three years ago costumers would ask us, “Hey, IP is not going to work for live TV.” We convinced them otherwise, and now they know it’s working, it’s happening for real.

Secondly, they are thinking, “Okay, now I get it, so how do I do this?” We showed them, this is how you do it, the education piece.

Now, this year, the number-one question is security. “Okay, this is my content, the most valuable asset I have in my company. I am not putting this in the cloud,” they say. And this is where another piece of education has to start, which is: Actually, as you put stuff on your cloud, it’s more secure.

And we are working with our technology providers. As I said earlier, the COTS providers are not equal. We take it seriously. The cyber attacks on content and media is critical, and it’s bound to happen more often.

Initially there was a lack of understanding that you need to separate your corporate network, such as emails and VPNs, from you broadcast operations network. Okay, that’s easy to explain and that can be implemented, and that's where most of the attacks over the last five years have happened. This is solved.

They are going to get right into the servers, into the storage, and try to mess with it over there. So I think it’s super important to be able to say, “Not only at the software level, but at the hardware firmware level, we are adding protection against your number-one issue, security, which everybody can see is so important.”

However, the cyber attackers are becoming more clever, so they will overcome these initial defenses.They are going to get right into the servers, into the storage, and try to mess with it over there. So I think it’s super important to be able to say, “Not only at the software level, but at the hardware firmware level, we are adding protection against your number-one issue, security, which everybody can see is so important.”

Gardner: Sure, the next domino to fall after you have the data center concept, the implementation, the execution, even the optimization, is then to remove risk, whether it's disaster recovery, security, right down to the silicon and so forth. So that’s the next thing we will look for, and I hope I can get a chance to talk to you about how you are all lowering risk for your clients the next time we speak.

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