 and events in the world of emerging information. Now, it's time to extract the data and turn it into action. Live from the SiliconANGLE Studios in the heart of Silicon Valley, this is Extraction Point with John Furrier. Hi, I'm John Furrier. Welcome to the Extraction Point with John Furrier. We have a special guest here, John Liddle, who's famous from MIT who invented Liddle's Law, which we'll talk about and what it means for computing. And my other guest is David Floyer, co-founder of Wikibon Research. We're going to go deep dive into the math behind big data. So John, welcome to the program. We appreciate you coming on. So tell us about what is Liddle's Law? OK, let's start with something simple. Taco Bell, a Dunkin' Donuts, Liddle's Law has to do with queuing. And we all wait in line, say, at Taco Bell to get a place our order, pick up our order, and leave. OK, so Liddle's Law deals with arrivals and rate at which people arrive. The average number that are there at any time in queue and the average weight per person in queue. So we have a relationship. It turns out that the average time in queue equals the average arrival rate of customers times the average weight in queue. And so in queuing terminology, L, which stands for line, and lambda, which happens to stand for arrival rate, and W, which is waiting time per person. So it's L equals lambda W. OK, this has become very much used in the world. And the two major areas have been operations management, which I'll come back to in a moment, and computers. Inside computers, there are a lot of queues. OK, and in computer systems, generally. OK, in operations management, you have a big movement a while back in lean manufacturing. OK, lean manufacturing was a set of rules or opportunities, places to look for in your manufacturing system where you might make improvements. OK, but this was guided. And in particular, I know when he's written books, a fellow named Mike George, who was very successful at this and built a company and sold it later to Accenture. So what he did was he said, what you want is low cycle time. You want to get the stuff out the door, manufacture it, and get it out the door. So that is low average time in queue. And that is equal to the, that's my, what's not that one actually. It's the average queue length divided by the transaction rate. And so what he did was essentially look at these potential for improvement. And he found the major potential for improvement is in reducing the queues, the average queues. And so he would scout around and find things in lean manufacturing which would reduce that. And that meant that he decreased what the computer people call latency. But I call average, and the operations people prove to be called cycle time. But it's essentially the time between producing the next item or manufacturer, airplane, whatever. And so this was a very successful operation. And it becomes clear because of Little's Law, because Little's Law is essentially a mathematical equation. You don't have to go out and check it in the field. It's true. And so it offers very strong guidance to people who wish to improve their systems. But it doesn't improve the system itself. You have to look at the terms of it and pick on the ones that you think you might affect and take it from there. David, you obviously have an interest in storage and operations around some of these new trends going on in the business. And storage has been the hottest area around cloud and this new social web. And so this is an operations equation. And often in the big data world, storage and Hadoop and the world we live in, the world data scientist comes in. But there's math involved. What's your opinion on the complexities and how Little's Law is vectoring into it? Because you and I were talking about Fusion IO, for example, some of the things they're doing with low latency. But moving packets around is a queuing theory. There's arrival times, there's transit. This is math. I mean, manufacturing, computing. How do you draw that together? So one of the challenges of computers has been the mismatch between persistent data, safe data, and the memory where all the operations are done in the computer itself. So you're talking about computers working in nanoseconds, memory in microseconds. And then you have the disk where you actually secure that data in milliseconds. That's 1,000 times longer. It's a huge difference. And so you get these queues. You get queues waiting for results to come back from the disk to make sure that it's safe on the disk. They can't do anything until that operation has taken place. What's exciting in the computer world is the introduction of flash memories, which can replace this persistent storage, this disk storage, and operate 100, even higher than that, 100 times faster. And the potential of that is to improve those systems, improve the queues on those systems, reduce the queuing time on those systems. And obviously, the trade-off is the cost, their higher cost, than the disk drives themselves. But by applying those to the system as a whole, then you should be able to increase, if I'm right on this, Professor Little, if you're reducing that latency, if you're improving the time on that operational side of things, then your potential is to increase the throughput through those systems. So I posted on Facebook today an article around that The Guardian wrote in the UK, and it was about the internet is over. And I want to bring this up because I think what's relevant about John being here today and this conversation is extracting out a little beeping going on here, extracting out the data around what this really means. The article is written around the South by Southwest big tech conference going on where The Guardian writer, a journalist, went down and said, I went down looking for the next big thing. This is a conference that's all about the next big technology trend. And he said he couldn't find it. And his whole article was essentially, it's upon us that the world of life, the layer of technology, is now integrated into our life, meaning it's already here. We're integrated into a computer lifestyle. So it's interesting about the Little's Law how it's been applied into manufacturing and, say, retail, you mentioned Taco Bell, queuing theories, lines, et cetera, throughput, operations management, that the computer internet is actually being part of our life. So this is a new operating cycle. And Jim Law, a friend of mine, wrote, the obvious trend except that gaming is leading technology even more than in the research labs. When cars and leading technology is now even more into our life, when cars and highways were invented, they went from cumbersome to an integrated part of our life with rest stops, gas stations, and repair shops. That is what is happening here with technology, although more personal and ubiquitous. So he's saying social networks are here. So this concept of data, the speed of data, the latency, is a big challenge for the lifestyle of the workers, productivity. So tie that together to the geek terms. So how does that fit into the life of a user and then have that relate to a company like an HP, a Fusion IO, an IBM, an EMC, et cetera? Shall I have a go at that first? Sure. You can comment on it. To me, what's really exciting about this is that previously, we designed the industry, designed computers around these constraints, this big constraint particularly on the disk drive. They had to do their best to get around this. And they invented lots and lots of different ways and techniques of getting around that particular problem. The exciting thing here to me is that when you're looking at computer architectures and you're looking at ways that you can improve it, the biggest single way of improving it at the throughput of it is going to be to reduce the latency. And it's a direct, if I understand your math correctly on this, it's directly related to it. So if you go from a millisecond to 50 microseconds, you've essentially increased the potential throughput. And then we have a new phenomenon with this called the real-time web, which is real-time data analytics. You have mobility. So John, you're out here still talking to some of the big companies that are impacting the social lives of people. OK, well, a couple of elements there. First of all, I'll corroborate this. Say how Little's law fits into what we've just heard. He's concerned about transaction rate. OK, in Little's law terms, transaction rate equals average Q in the system divided by latency. So if you want to up the transaction rate, you reduce the latency. You improve the latency. And so that's Little's law expression of what we've just heard. So I don't want a personal note. I want to ask you an opinion from you. You're out talking to some of these young developers of some of the most biggest, most growing companies in Silicon Valley. I won't say their names, but they're in Silicon Valley. And Mountain View, Palo Alto, Mountain View. And these are the new franchises in technology, the big new names. And they're impacting hundreds of millions of people with this new technology, these social technologies. And most of the engineers are young. What have you found in talking to some of the younger generation of computer science folks? Well, I found that the guy sitting next to me was from MIT. Did they know who you were? He asked me about it, an electrical engineering professor that I know. Are the kids young? Are they just unconsciously competent? What's your impression, I mean? Well, they go where the excitement is, and the best people. Are they feeling Little's law? For a long time, well, after I spent my time on Little's law this morning, the MIT guy went out, and he got in a big discussion with another engineer. He said, that gives me some ideas. So you're creating a lot of provocative kind of questions for these folks to think about. Well, I had them prove Little's law. Did they prove it? I gave them an easy case. Yes, they proved it. Cool. Well, technology, the extraction point here is that technology is changing. The platforms are growing, layers upon layers, like trucks and cars, had highways, and then rest stops came. More and more layers are building on our technology world, and it's complicated. And the bottleneck in all of this is storage. And we keep on coming back, David, to storage. Good old boring storage, or storage as Dave Vellante was saying, but it's a sexy part of the equation. I mean, it is a bottleneck in the operations cycle. And companies that are looking into this flash, like FusionIO, have the potential to make a huge impact on the architectural design of the systems that are going to come in the next 10 years. I have to go on a slightly different subject. And that is this change in all our lives, or most of us. And that is the time we spend on the web with places like Facebook. And I don't know what that's going to do to our diet and our health. But I have to make a slightly different observation, which was made by Tim Berners-Lee, the inventor of the web. And he said he finds Facebook a little alarming. He said he spent his career trying to design the web so that nobody could take charge of it, and particularly he had in mind governments taking charge of it. He says, but Facebook is a huge private venture with which is growing by these. As is Twitter, basically. That's right, Twitter, Twitter. These are social utilities that are integrating into people's lives, and they're not open, technically not open. That's right, and that's really very interesting. Is there any views around some of your colleagues at MIT and around the world around how these operations could be transformed and be quasi-open? Is there any? That's the open source. And MIT has what it calls open courseware. It decided early on there was a lot of notion and education a few years ago. But universities essentially running big educational operations, and MIT decided no. It wouldn't. It would essentially give away the software and video and what have you that was in MIT courses to anybody in the world. Let's talk about some future. And just to reiterate, Wikibon is about an open source of research where we give away the research. Wikibon.org is an open research platform with contents free. Content is free, exactly the same philosophy as MIT. And our business is improving that research. As is in SiliconANGLE.com and SiliconANGLE.tv, of which we have to lower our latency and increase our transactions. We will apply Little's law certainly to that. Question on the future. So this is going to make you uncomfortable, I'm sure. But this kind of gray area is not black and white. But you talk about Taco Bell. I think about Taco Bell. I think about banks and bank tellers. And I think about McDonald's and In-N-Out Burger and all those franchises. They all have drive-thrus. But back in the day when I was a kid, not everyone had drive-thrus. So someone must have applied some Little's law and said, hey, we have people in cars. We can put drive-thrus in. And all that math that goes into cycle times and adding another teller, it's a civil operations theory, is what has happened. Will there be something like a drive-thru that's going to be a breakthrough in our social lives with computer technology? Is there any vision around these new latency busters? Any things out there that might create better latency for us in our life? Is it mobile phones? Is the mobile phone an opportunity for transactional efficiency? It's certainly going that route. Dave, you have any vision on there? Just speculation. I mean, it's something I think about. It is. If the quicker we can do things, if you apply it to our own lives, the quicker we can do things, the more things we can do in our lives. And if you can do the transaction quicker on your phone, on the move, when you've got downtime, yes, it has a direct relationship to the potential quality of our life. Whether we choose to spend it to improve the quality, that's a personal decision. Well, I find that with all these things that I can do so quickly and fast with email and on the web and what have you and communicate with my students why I can work 24 hours a day. Dangerous topic. We all are mobile. We're here at the extraction point with John Furrier, with John Little from MIT, who invented Little's Law Pioneer and what's called as Marketing Science, which is quite popular these days with the access to data and David Floyd, your co-founder of wikibon.org, a cutting-edge research firm that gives away its data for free. Final question for John Little, obviously, your career, certain story career, you have a great reputation. And you're out talking to all the young engineers and young guns in the computer industry. Is there any advice you can share with folks out there that are watching that either may go to MIT in the future or have gone to MIT or are inventing the future? Is there any advice you want to convey to them? I think they're doing fine. And my only advice is, well, at school we used to say that there were 24 hours in the day. And the first shift was for your homework. The second shift was for your job. And the third shift, you could do anything you wanted. So advice is, use your shifts, do whatever you want, be creative, invent the future, apply Little's Law, increase your transactions, whatever that may be. Thanks for coming on the extraction point. We appreciate it. And we're going to come right back and do a deep dive with David Floyer and get into the deep dive data science behind some of Little's Law's applications. So thank you for watching. Ricky, that's a wrap.