Transcriber: yoojae lee Reviewer: David DeRuwe
My mom is 76, and I am her tech support.
The most frequent issue she runs into with her iPad and smartphone is data.
She has accumulated so much data that she’s running out of space.
Now, on the other end of our generational spectrum,
here comes my teenage daughter,
who took 50 pictures of the exact same pair of her Converse shoes
just to be able to pick the best one to post on her Instagram.
Consequently, my family's entire iCloud storage was used up.
For me, this is not just a personal problem, but also a work challenge.
You see, I work at Facebook as an engineering director,
and it is my job to ensure there is enough space,
which is what I call cloud capacity, for every user.
Today, my company has about 2.6 billion users.
Let’s imagine each of these users uploaded one photo.
If I print these photos into our four-by-sixes
and stack them side by side,
the prints will have enough
so that they could circle our entire beautiful Earth
not just once, but a total of 10 times.
To support these users, we not only need to store all the data,
but we also need to process them, which means we need computers.
Lots of them.
A technology research company called IDC made a prediction
that data stored in the cloud would reach 100 zettabytes by 2025.
That is a 100 trillion billion bytes.
At this rate, simply adding more computers will no longer be a sustainable solution.
You may wonder:
Data has been growing fast like this for quite a while,
so why is it a big deal?
You’re right.
I was never worried because I had a superpower in my pocket,
and it’s called Moore’s Law.
So what is Moore’s Law?
It is a prediction that says computers get faster for the same cost.
In 1965, Gordon Moore, the co-founder of Intel,
predicted that the number of transistors
on the silicon chip would double every year.
The semiconductor industry took his prediction as their North Star,
and they met the challenge.
Ten years later, Mr. Moore revised his prediction
for the number of transistors to double every two years.
The semiconductor industry met that challenge again.
When I first came to the US for graduate school in 1994 from Inner Mongolia,
my professor told us about Moore’s Law,
and he basically said there’s no need to worry about how to keep up
with the growing data because Moore’s Law was taking care of it.
This has been true for the last 50 years till recently.
There is now a physical limit of the silicon chip getting reached,
and therefore, we now start to see the slowdown.
To give you a rough example,
on a silicon chip the size of my fingernail,
there can be up to 50 billion transistors.
50 billion - that’s 10 times the number of trees we have on this planet.
Recognizing that Moore’s law may no longer help us as significantly,
the entire cloud infrastructure industry worked together
looking for new methods to bend down that demand curve.
So here you can see, this green line here, indicating our computer supply,
and this blue line is the increasing demand to store and to process the data.
As you can see, the supply and the demand are mostly kept up,
but you also start to see a gap, and this gap is widening.
This means my dear mom may have to delete her precious videos and photos.
This means you and I may have to pay more for our iCloud storage.
This also means more and more data centers are going to be built,
consuming more power, cooling, and space on the planet.
Luckily, my colleagues and I do have a few methods here,
each of them bending down the demand curve a little bit at a time.
And here I’d like to share three of them with you:
The first method we use to bend the demand curve
is by introducing hardware accelerators.
Hardware accelerators are like specialists in the hospital.
While a general doctor can be very good triaging and admitting a lot of patients,
she scales herself by referring specific patients to the specialist.
In the data processing world,
these can be video encoding
or an intensive artificial intelligence calculation.
Adding the hardware accelerator to a computer
is like adding specialist support to a general doctor,
and working together, they become much more efficient
by focusing on what they’re best at.
To give you a rough example,
if I tell my computer to recognize the image of my daughter’s shoe photo,
it would have taken about a day using a regular CPU,
but with hardware accelerators, this can be done under 30 minutes.
Hardware accelerators help to bend the demand curve.
The second method to bend the demand curve
is by using software to optimize our cloud platform.
Now, platform optimization is like playing the game of Tetris.
The different colors of tiles
are like different types of computers in my data center.
Some are for storage, while the others are for compute.
I would spread and stack them
so that there’s as little wasted space as possible,
which means I get to have less idle computers and a higher utilization rate.
This Tetris game is so fancy that it’s even 3D,
which means I get to leverage time zones.
When the users from the continent of Asia went offline,
I can possibly use those computers to process data
for our North American users.
And just like any gamers,
I would keep track of my gaming scores from one day to the next,
and each day I get to bend that demand curve a little bit more.
Now the third method, also the newest, is to change how the data is used,
therefore, where the data is stored.
For example, there is a technology called “on-device compute.”
Instead of uploading all the data up to the cloud
and then process them up there,
we would process the data locally on your devices,
either your iPhone or your laptop,
and from there, only a subset of the data
after processing gets uploaded to the cloud.
If you imagine my data centers literally as a box,
this approach is literally thinking outside the box.
By reducing the amount of data uploaded onto the cloud,
we get to bend that demand curve a little bit more.
To sum it up, as you can see, there are multiple technology inventions
being used to address this steep growth curve of data.
As a result of applying hardware accelerators,
software optimization, and on-device compute,
all the cloud computers are holding strong for all that usage.
If I can give you an example,
in the last eight minutes or so since I started this talk,
more than 4000 hours worth of video got uploaded to YouTube,
about 120 million messages were sent by text,
and my daughter may have just uploaded another 50 photos of hers.
Keeping up with all the data growth will continue to be a challenge,
but I am optimistic that we will continue to have
new inventions being developed along the way.
I’m committed to support all of my cloud users,
ensuring there is enough space to carry their stories.
I’m committed to be my mom’s tech support, guarding her photos and videos,
but I do plan to go talk to my daughter
about her thousands of the same shoe photos.
Someday, even when I no longer have the support from Moore’s Law,
I’m confident that my colleagues and I
will find ways to operate millions of our cloud computers,
and operate them well,
because these computers are indeed so important to us.
They carry not only all the data, but also our memories,
our laughter,
and the stories of our lives.