What makes WiFi faster at home than at a coffee shop? How does Google order its search results from the trillions of webpages on the Internet? Why does Verizon charge $15 for every GB of data we use? Is it really true that we are connected in six social steps or less? These are just a few of the many intriguing questions we can ask about the social and technical networks that form integral parts of our daily lives. This course is about exploring the answers, using a language that anyone can understand. We will focus on fundamental principles like “sharing is hard”, “crowds are wise”, and “network of networks” that have guided the design and sustainability of today’s networks, and summarize the theories behind everything from the social connections we make on platforms like Facebook to the technology upon which these websites run. Unlike other networking courses, the mathematics included here are no more complicated than adding and multiplying numbers. While mathematical details are necessary to fully specify the algorithms and systems we investigate, they are not required to understand the main ideas. We use illustrations, analogies, and anecdotes about networks as pedagogical tools in lieu of detailed equations. Please note that per Princeton University policy, no certificates, credentials or reports are awarded in connection with this course.
Demand for data
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Networks Illustrated: Principles without Calculus
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From the lesson
Pricing Data
Data makes up a significant part of our cell phone bills. How do cellular providers set these price points? In this lesson, we will see how so-called usage-based pricing schemes can send better signals than flat- rate, “buffet” schemes, leading to better sharing of the network.
Meet the Instructors
Christopher BrintonLecturer
Electrical Engineering
Mung ChiangProfessor
Electrical Engineering
So, the first pricing method we're going to look at is the way that cellular
data was done for awhile, which is called flat rate pricing.
Right. So, you pay some fixed amount and you can
consume as much as you want. And, you can think of a buffet for this.
It's a very, very good analogy. So, when you go up to a buffet, you just
pay some amount to whoever's at the front.
You know? Say you pay $20, and you can get into the
buffet and then you're allowed to eat whatever you want.
So, you can eat as much as you want. And so, if you're really hungry, that's
great, right? It's very convenient for you.
But the problem is that if you're not as hungry, it's not going to be quite as
great, because you wouldn't want to pay $20 for instance if you were just getting
one piece of bread. But if you're paying $20 for a huge, huge
meal, and that's obviously much more cost effective.
And so, but now buffets aren't necessarily the most scalable thing in
the world, if you think about it. Because there's only so much food that's
in the buffet, so, what would happen if people kept consuming more and more food,
right? So, every time someone came back, they
had more and more of an appetite, and you have to think about that.
And the problem is that there's only so much food that the buffet can offer.
So, if people kept going back and saying alright, well, hey, I'm going to eat as
much as I want and everybody kept getting more and more hungry, because they kept
being able to eat more and more. That would eventually become a huge issue
for the buffet. And so, that's kind of an issue that we
started having with cellular data, just a little while back, not that many years
ago, is that, these data applications we're using became so intensive, right?
And so, you'd think about the buffet as the cellular network, right?
And there's only so much capacity in it for instance that can actually serve what
we want to do. And so, the problem is that as these
applications kept getting more and more intensive, this wasn't a very scaled
model any more. Because people were able to come in and
use these unlimited data plans, and we can use the buffet example again.
You come in and you're only paying a fixed amount, then you can consume as
much as you want. So, as people's appetites start growing
larger and larger and larger, that becomes much more of an issue for the
buffet itself. And then, [INAUDIBLE] buffet might say
okay, well, now if you want to come in you have to pay $30 because I have to buy
more food, and I need that to supplant the cost.
But again, there's a lot of issues with that in terms of raising the price for
everybody just based upon maybe what only a few people are doing.
So, we can look now at the demand for data.
As you said, the flat rate was okay, initially.
the problem is that Smartphones came out. And with Smartphones, we have the ability
to surf the web and stream videos, and do all these things that weren't possible
before. Right?
So, data demand just soared. So, initially on phones, right?
And most people still may think of phones this way.
It's hard to tell anywhere really, that initially with cell phones, at least the,
the primary use was for voice. Okay.
So, voice was the primary use, and data was only something secondary.
So, offering a flat rate per data was okay, because people weren't really
consuming as much data. So, you'd say okay, we'll tag on $10 for
whatever you're paying for your voice, and you can use whatever data you want.
And initially, it was only really simple web surfing for instance, like people
would surf the web. But now with smartphones, it's so easy to
surf the web, so easy to stream video, so easy download intensive applications,
stream music from online and so on. And these are really, really data
intensive, especially streaming videos. Videos are huge, and streaming videos are
really large, especially if you have a decent bit rate on your phone.
And that can really suck up and eat up your data you're using.
And so, for instance this is a pie chart from 2011 showing the different fractions
of data that were being consumed by different applications.
So, video streaming was already up to almost 40% by then.
And you can see, how much data video streaming really took up.
And as I said, streaming videos is very very intensive, and so, that's taking up
quite a, a lot. And every year that goes up a lot, the
amount of streaming that we do on our phones, and especially with 4G LTE.
And the data rates that that can offer, these video streaming going to become
much more predominate. But still even so, things, the file
sharing is still takes up some data. Web browsing can depending upon what
you're doing. And so, again, we, there's just a lot
more data intensive applications out there now.
And this really all started when, around 2007 with the iPhone.
We have to really credit Steve Jobs here, when he introduced the iPhone at at
Apple. And the iPhone in 2007 caused a 50 fold
jump in demand, just that itself. In 2007, there's just a huge demand in
the amount of data that we needed, is because the iPhone, the smart phone just
let's you do so many more data intensive things.
And very conveniently. Right?
So, app [UNKNOWN], app developers began to push out these apps, and these apps
would make it so easy for us to just stream data and use our three.
At that time just 3G, but now 4G connections to do things like that.
So, now, before we move on, we have to think, well, how do we really measure
data? And what does that thing called gigabyte
really mean? Well, a byte is the way that we measure
data. It's the fundamental unit for it.
And that's really fundamentally defined in terms of bits.
And you remember bits we talked about before.
It's just a sequence of ones and zeros. So, a one or a zero.
This is a bit. And so, if we put eight of those
together, one, two, three, four, five, six, seven, eight.
For instance, you know, it doesn't have to be [UNKNOWN] like that, but if we have
eight bits that makes 1 byte. But a byte is so small compared to what
we do on the Internet, that we talk about megabytes or gigabytes of data.
Just like we talked about megahertz or gigahertz and and so on.
With that, we talk about megabytes or gigabytes, typically, gigabytes is what
we'd hear, and that's why we're saying $10 per gigabyte in this lecture.
Just to give you an idea, you're probably wondering well how big is a gigabyte.
Well, if you stream, you could stream about half of a standard movie for one
gigabyte. So, one gigabyte is about the size of
half of a movie. So, if you had two gigabytes, that's
about one movie worth of data. And standard definition not even when you
get high-definition, it's something like double that even.
So, it takes two gigabytes to store one movie.
So, just think of it that way. Now, every month, this is the projection
after 2017 with the amount of data. Okay.
So, this was in 2010, it was 0.24. 2011 0.6, and all the way up to 2017,
which is supposed to 11.2. But notice that we're not measuring this
in gigabytes by any means. We're measuring it in Exabytes.
So, what is an exabyte? Well, an exabyte is ten to the ninth
gigabytes, which is 1 billion gigabytes. Which is 10 to the 18th bytes.
So, that's how many bytes you have with ten to the 18th zeroes after it.
So, in 2017 this demand is expected to be 11, and then with 10 to the 18th zeroes
after it. It's huge, exabytes is just a huge amount
of data. Think about this 2 GB we said was equal
to about one movie. So, in 2013 this is about 2 Ebytes.
We had about 2 billion movies going through the data, the mobile data every
month in 2013. That's what at least the projections for
the end of the year are supposed to be, and that's expected to climb up to 11.2.
Now that is just astronomically large, and these demands just keep going up and
up, larger and larger. That's part of the reason these flat
rates are not really okay anymore. Because people can't get away with just
paying for instance just $20 a month to consume how ever much they're doing.
The network just can't handle that.
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