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Из курса от партнера Princeton University

Networks Illustrated: Principles without Calculus

39 оценки

Princeton University

Networks Illustrated: Principles without Calculus

39 оценки

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.

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Movie Recommendation on Netflix

One of the perks of having a Netflix subscription is getting recommendations of movies to watch. Behind the scenes, Netflix uses powerful algorithms to determine which will be suggested to each person specifically. In this lesson, we will take a look at the main ideas behind these algorithms.

Netflix Timeline5:21

Video Streaming5:19

Recommendation is Everywhere3:24

Netflix Recommendation System5:20

Netflix Prize: Logistics7:38

Netflix Prize: The Competition4:33

Our Example3:48

Raw Average7:13

User-movie Interactions7:00

Baseline Predictor6:57

Cosine Similarity9:18

Similarity Values10:58

Leveraging Similarity8:30

Neighborhood Predictor4:44

Performance of Different Methods3:30

Copy of Cosine Similarity9:18

Познакомьтесь с преподавателями

Christopher Brinton
Lecturer
Electrical Engineering
Mung Chiang
Professor
Electrical Engineering

Then Netflix moved into the idea of video streaming, where you could stream movies

or TV shows to Internet connected devices.

So rather than having the store where you would need to go for rental or an online

store where they would ship a DVD to your house, and we're talking about the

physical shipment of goods or picking up of goods.

Then they moved into what's called video streaming, and that's probably what you

know them as more recently, if you're subscribing to Netflix, where you can

stream movies or TV shows to Internet connected devices.

So the way it works on your TV, set top box, STB stands for set top box smart

phones. So this is your, in this case we're

talking about a set top box. You basically have the Internet, right

and your, your box or your TV is connected to the internet in some way

through your house. We've looked at the WiFi set up for a

home and this is in a wireless connection on your set up box.

It would be on your Smartphone but your set up box not to be wireless but you're

connected to the Internet somehow. And then the Internet has a connection

back to you. And then somewhere from within the

Internet there's some place this we call these servers or they're data centers and

we a lot of times think of that as the cloud.

And there's there's another chapter in the optional reading on the cloud that we

may have time to look at a little bit in a later lecture.

And then basically, once you send this request to this cloud then the cloud will

stream the service all the way back to you.

So this movie that you want if you're selecting whatever movie this is.

Is contained somewhere in this, server and then the server will, send that movie

back to you in little bits and pieces as you watch it, allowing you to watch the

movie. So they started moving into that, this

whole idea of streaming movies. And that was, really gave them a lot of

remarkable growth. And there's a lot of statistics up to Q1

2011 around March actually. If you look online there's some stats

about Netflix in March, 2011, when they did some tests on the Internet and

packets running from the Internet and so on.

so one of those is that the data increased to 23 million subscribers in

the US and Canada. By March of 2011 and they had from 2008,

they only had 9 million so they basically had almost tripled in size in about three

years which is pretty remarkable. they had also surpassed Comcast so this

time at least they were right up to where Comcast is or up to par, just a little

higher than Comcast in terms of their number of video subscribers.

And so, now you can see Netflix over here had about 22.8 million [UNKNOWN],

millions of subscribers. Comcast, 22.76 is trailing behind a

little bit. And so they had, they had moved up to the

top. And this is only counting the people who

are subscribed to Comcast for video services, not necessarily Internet

connectivity for instance. But then, you know, moving down we also

have Direct TV, Dish, Satellite Network then these files down here.

Then they've got, this is as of 2011, so obviously two years ago.

And then AT&T, U-Verse over here down with 4 million, 3.8 million respectively.

Some of these are growing very rapidly too and but at this time at least they

were at the height and they were the highest.

And really cool is that if you took at that time in March 2011, whenever this

was done. If you took the number of packets that

were on the Internet and you basically counted up the number of them that were

going for different services, okay. Netflix had 1 4th of the bits on the

Internet which is really, really interesting.

So really, what we mean by that is right here we're dividing things in terms of

upstream bandwidth, okay. And upstream means going from the

customer to wherever it was in the Internet.

Then we have downstream which is going from the Internet to the customer, right.

So if this is customer and this is the Internet, or we're never in the internet

really. the application we could call it.

Basically, upstream is from the customer to wherever.

And downstream is from wherever it's coming from back to the customer.

And if we took all across the Internet and and looked it over to see the

estimate was that Netflix in downstream which again is coming from Netflix to the

customer, had almost 30% of total downstream bandwidth.

So that means that 30% of the bits on the Internet were actually coming to Netflix

or from Netflix to customers. Which is really, really insane.

And again, large part of the reason for that is because as we say video data is

huge. And especially HD movies they require a

lot of bandwidth to serve, so it does take up a lot of room on the Internet.

And at the same time aggregate is a combination of upstream and downstream.

Just looking at all of everything that is being sent over the Internet.

And they had at that time 24.7 or about 1 4th or 25% that's where this 1 4th comes

from. So what it's saying is that one in every

four bits on the Internet in the month of March 2011 was actually coming from

Netflix. And so this is to show you how large

Netflix was in terms of the demand that they were taking from the Internet.

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