[ Music ]
>> Hello everyone.
Welcome to day five on Bits and Boymers stage.
It's so nice to see all of you even though you look a bit more tired
than two days ago when I last saw you.
But I hope we are all going to look as sunny as this day when this talk is over.
Speaking of a sunny day, please take care too.
Do you know?
Drink water, yes.
And use sunscreen and be in the shadow.
Take breaks, you know.
Don't just run from talk to talk.
Take breaks.
Sit in the shade.
And also look that people around you are doing the same.
Yes. And if you have any thoughts or comments, you can also always post them
on Mastodon using the hashtag #bitsandboymer.
Boymer with a E. So, and now I'm really happy that I can announce our speaker, Thomas.
Thomas is mainly working with clouds and cloud security.
And when he was looking for energy consumption of data centers,
he didn't find enough information.
So he went looking for them.
And he's going to present them to us today.
So give a warm applause and welcome to Thomas.
[ Applause ]
>> Thank you very much.
So this is what I'm normally doing.
Kubernetes, cloud security, and this stuff.
But let's start.
So energy consumption of data centers is something which is not really well published.
So the data center owner are not necessarily publishing a lot of numbers.
And you see there are a lot of articles and the best ones I have presented here.
And this study here is from 2020.
So they show an energy forecast which is exponentially growing.
And always if you see something exponentially growing, you should be very,
very careful and aware that something could terribly go wrong.
If you compare the data center usage with the energy consumption of entire countries,
you see all the data centers are more or less in the range of South Africa,
even comparable to the UK or Indonesia.
So if the data centers would be a country, you see it's a bigger country.
If you look into the German numbers, you see we are spending 16 terawatt hours on data centers.
Mostly the heat which is produced by the CPUs is not used anymore.
There is no decarbonization possible because it's just heat.
And it is at the moment 3% of the electric power consumption.
And the dangerous part is it has an annual growth of 6%.
This means it will double probably around every 12 years.
So if the data centers from now in the camp, if we talk again, it's a camp in 12 years,
which would be 2035, then the data centers have doubled the energy consumption
if this trend continues.
For all of you who are not familiar with exponential growth,
here is something which is growing exponentially.
The first thing, first picture is just radioactive decay.
So this is a chain reaction or, with other words, it's an atomic bomb.
And the bomb explosion stops if all the fuel is spent or something other is limited.
And another example for exponential growth is the spread of COVID.
In your body, the virus load also increases in the first steps exponentially.
And the limit is your body.
If your body cannot provide more resources for the virus,
then the exponential growth stops.
The same with the population.
We had this in COVID and this is the danger of something increasing exponentially,
that you simply have a limit which you don't want to hit.
And here you see, this is the green curve,
is the growth of the data centers predicted for the next years.
[COUGHS]
And this makes it dangerous and so something limiting will happen.
We don't know exactly what.
It could simply be that they go out of business because the price is too high.
It could also be...
Oh, sorry.
It could also be that it is competing with other resources
and that simply we cannot spend the energy we use in data centers on other things.
Here you see the comparison of data centers with aviation shipping.
And here it's getting interesting.
It's even competing with food production.
So...
And the range is because the data is not really clear.
The data center owners do not really need to give out the data.
If you look into...
If you run your data center on your own or if you should go to a cloud provider,
the cloud providers, the hyperscalers...
[COUGHS]
The hyperscalers are the best in terms of energy saving
because they have the biggest data centers.
This is normal.
And so from an energy perspective, the advice would be go to the cloud
and don't build your own data center.
[COUGHS]
Oh, sorry.
This is obviously competing with privacy.
So you have the option not to go into a cloud
and have GDPR-compliant co-location or your own data center
or you have to save energy and go into the cloud.
If you compare another thing,
then you see it's not only consuming energy, it's also consuming water.
So the 15 megawatt...
[COUGHS]
The 15 megawatt data center is around the energy consumption of a hospital
or an Almond Orchard.
And not the energy consumption, sorry, so the water consumption
or comparable to a golf course.
So if you have the choice to build a data center, a hospital or a golf course,
then it depends on your preferences, what you prefer.
Data center is just for maybe computing, gaming.
Or for the hospital, yes, this is just for the normal population.
And if this competes, you have immediately a conflict.
And normally, the best lobbying wins
or the best use of venture capital.
And I don't know how much venture capital goes to the hospitals.
If you see here, there is some number which is always used for advertising.
This is power usage effectiveness.
It has been invented by Google because Google looks very good with this.
And you see here, it's a number and they improve all the time.
And this number is simply the total energy consumption
by the real load which is used for computation.
This means if the number would be one,
then it would be a 100% effective data center
without need of cooling and energy for cooling and infrastructure.
And if always, if you see the number, it's like in the...
Another example is if it's in the Deutsche Bahn,
the Deutsche Bahn train shows you only the speed if it's fast.
It does not show you if your ICE is spending 30 kilometers per hour.
It shows you if it is going with 200 kilometers.
And so if you see the numbers, normally they are good.
You don't ever see the bad numbers.
And here is another curve which shows how Google makes it.
The energy, the water consumption of Google data centers is much higher
than the water consumption of other cloud providers.
So and then they compare obviously with others,
with countries, with other cloud providers.
So you should take these numbers with care
because on this scale, a renewable energy consumption,
Amazon is better than Germany.
And I simply don't believe these numbers.
So resource conflicts,
they are popping up everywhere where data centers are built.
In Virginia, people are gathering against an Amazon facility
with a consumption of 600 megawatt.
600 megawatt is more or less half a power plant.
In Ireland, the data centers make...
They take 18% of the energy consumption of Ireland
and the national strategy of CO2 reduction
now is going to cull 65,000 cows
because they want to limit their carbon dioxide production.
So you have here data centers against cows.
And if you remember Ireland, in my memory,
Ireland was also very famous for herding cows and sheep.
And comparable, the data centers are using as much energy
as all the private city homes.
In the UK, they have in East London,
created a big environmental opposition movement
because they cannot do housing anymore.
If you don't have energy for housing
because you want to sell the energy to the data centers,
then this is the next problem.
So always where these huge data centers come on a local scale,
you have this kind of resource conflict, energy and/or water.
Same in the Netherlands.
And they even talk about that they need more dikes
so that they build higher walls against the North Sea
because if they pump up this water for cooling
and vapor it, then the groundwater is sinking.
And this means they need to put more energy in moving.
And then the next step is the saltwater comes from the ocean
and then they have other conflicts too.
This is another example in the Dallas in the US.
This is on the local scale, it's also an exponential growth.
So this means typically you can compute,
you need between 1.5 and 2.3 liter water per kilowatt hour energy.
This is typically 15 to 23 cubic meters for a 10 megawatt data center.
And is this a big one or not?
So I looked it up.
The typical sizes of data centers start from 15 kilowatts.
So this is a, the racks fit into a container.
The typical things we have here from NTT in Berlin or otherwise
are between 5 megawatt and 40 megawatt.
And the hyperscalers have more or less no limit.
So the biggest I've read about is 600 megawatt.
And the typical water use is between 4 and 7 million cubic meters water per year.
This is a medium large city for a data center.
And the first stops have arrived.
So in Ireland, Microsoft and Amazon have to hold their plants.
In the Netherlands too.
In Germany, in Brandenburg, we had the problem that Alphabet,
so the company behind Google,
is not allowed to build a data center where they originally have planted.
It has now moved to Mittenwalde.
The data center in Neuhagen would have been optimal by energy usage
because there is a power switch station.
But where they build it now, it's more or less in the middle of nowhere.
And they have no use for the heat and something like this.
In Neuhagen, they have planned a PoE value of 1.03.
And in Mittenwalde, they don't tell the number.
And one remark, all the studies I've seen so far
are before the artificial intelligence boom took off.
So what we know so far is that training chat GPT uses 1.
Oh, God.
1,287 gigawatt hours.
So roughly the equivalent of 120 US homes for a single year.
This means this comes on top of the situation here.
So the artificial intelligence is in the training phase using a lot of energy.
And here you also can compute this PoE value,
and this is a very bad one.
Here you see what you all have to take into consideration.
So CPU RAM and the GPUs.
And the GPUs are normally very similar to the Nvidia graphic cards
you run in your gaming computer at home.
And they are extremely energy hungry.
So here are some more numbers.
And the interesting part is here.
On an average, you can estimate by the computing cost the energy consumption.
So here are typical hardwares.
TPUs are tensor processor units.
So these are special graphic GPU-like devices for artificial intelligence.
And here you see the price.
And here is the equivalent for carbon dioxide in Ips.
So this is an American thing.
It's sometimes hard to calculate it in normal units.
More interesting is the comparison with real-life situations.
For example, here you see the consumption for air travel
between New York and San Francisco.
This is what you normally consume in one year, or what a US citizen consumes.
This is an XZEP36, 1156 Ips.
And a car for the full lifetime.
And here, there you see the training of one model with a GPU.
This is a neural processing pipeline.
It doesn't look so much, but you have to tune it,
and you have to do a lot of experiments.
And this means this increases to that number which is comparable to a car
in the entire lifetime.
So this means training in an artificial intelligence model
is comparable to running a car for the entire lifetime of a typical car.
Compared to that, Bitcoin is the worst.
It's a total waste of energy.
It is consuming more energy than some even developed countries.
From my personal experience, we had always these things.
Some developers come with a genius of an idea,
and they want to go live in critical infrastructure
so there is no cloud involved.
For example, a trading software which should be run on OpenShift.
The vendor came up with 10 nodes of HBase.
HBase is this Google-like database you can host for free.
It's an Apache project.
And then we tried to measure the load and calculated the real needs.
And finally, it was hard to detect the load.
And this means even my SQL database would be oversized.
And finally, we could have run this on an SQLite database on a Raspberry Pi.
And this means we have a trend in software that it has an incentive
that the team which spends the most resources is preferred.
So another experience from 10 years ago
was I have automated the database rollout for a customer,
and then we don't need an Oracle team anymore.
And yeah, Oracle teams are expensive,
but they immediately looked for Hadoop,
which means they have a huge cluster solution for a big data file system.
And so they could use more resources in their daily job.
And this is a problem.
I call it server machism.
The more horsepower, it's the same as imposing with expensive cars.
The more horsepower, the more important is the team.
And you should rethink your big data paradigm.
In most of the cases, it's completely wrong.
If you have to increase the size of your data to compute it,
then something is completely wrong.
I've seen customers doing this.
And on the other side, this idea cuts totally into the business model of data centers
because they want to sell you compute power, storage, network power.
And not only the hyperscalers have this problem,
but also the normal ISPs and companies who do the housing.
In the background, you could see one of these electric cars
which can go a thousand kilometers without loading.
It's completely useless because this is not a use case.
We don't need these big things anymore.
We need electric bicycles, but not electric cars.
But this is not really visible at the moment.
[APPLAUSE]
Thank you.
Another effect I'm responsible for is this so-called rebound effect.
If you make something more accessible, it's used more,
and then the consumption increases, and then the energy consumption also increases.
I looked it up and it is known as Yevon's paradox.
It was detected for steam engines in the beginning.
So if the cost increases, let's say from 100 to 80,
then the usage increases more than this amount of 20%.
So it goes from 10 to 40, so this is 40%.
This means if you make something cheaper, it's immediately...
use more. This is logical and this absolutely makes everything complicated.
If you make something easier, people are using it more than proportionally.
We have the same in the cloud and my personal observation is I'm doing
Kubernetes so I try to make cloud access as easy as possible and in this cloud
access there are hidden costs which I without exactly knowing them shrink.
So if you can do self provisioning you are unable to use the cloud which is my
daily job or do virtualization containers Kubernetes and automation.
Finally more people have access to the resources and this means effectively you
are triggering an increase in resource consumption. Even if the effective
container might be smaller than the virtual machine or what you have
before, finally the overall, the total energy consumption may increase.
And this is a problem we should have in mind. Further problems if we don't have
any transparent information on data centers. We only know roughly
hyperscalers are better than self-run data centers. Here's another bad old
example. So this is Berlin Spandau. If you know it here is the old E-Shelter
data center. Here is the Finanzamt, so the tax office Spandau. And here is a lot of
cooling devices. And on the other side of the channel there is a coal power
plant which also produces heat for the local heat network. And they are
completely disconnected. So the heat of the data center is wasted and on the
other side of the channel we produce heat to get our homes warm. And I've
criticized it even on the last Bits on Boehme conference. NTT has now a new
data center in Berlin Marienpark. They have two buildings and they have
connected up with the GASAC. So this is a, they are not only providing gas but
also heat for housing and for the industry. And they have a total renewable
data center with in the final stage 48 megawatt. The PoE is smaller than 1.3.
And they are based 100% of renewable energy. And this produces 29 gigawatt
hours a year. So this is a reasonable solution. So at least it's not wasted.
But it is not possible to use the old data centers and simply attach the heat
transform. Further topics here from Germany. We have drought everywhere. So
problem is if I want to cool my data center with local water, I need water.
And if the groundwater is sinking, I don't have this water. So Frankfurt has
this problem at the moment. And they have all over this reported these problems.
In Brandenburg, Google had also these problems. And it is absolutely unclear
if they can use it. If it's in the middle of nowhere of Mittenwalde,
probably there is no heat consumer. So definitely they will also waste the
energy. So this is a Google case. What you can conclude from the public
available information. And they don't tell you the PoE value, which means it's
a bad value. Otherwise they would tell you. Probably it's in the vintage data
center location because Google is not always hosting their own computers in
their own data centers. So the new energy law, it is valid only for new data
centers, not for the old ones. And the PoE must be below 1.2, but only from
2026 on. By the way, the PoE can run. I will give an example soon.
Data center must have a power of at least 200 kilowatt, which is not a very
big one. It has been diluted by lobbying. 300. Okay. So it's even worth 300.
Thank you, Anke. And a lot of lobbyism took over. And they must integrate it
into local heating systems. But in Mittenwalde there are no local heating
systems on that scale. There is a Golem article on that. So we have this
problem. And I don't want you to go without positive hope. We have the
ability of building very small data centers which can integrate it in your
local energy and heating system. For example, by the Hochschule Osnabrück,
they have created on their campus data centers in real industry containers.
So you can rack it. You attach it to energy and to the network, and then
it boots. What is booting a form of OpenStack? From the sovereign cloud
stack. They reported it on the summit. And you can integrate these small
containers in your local heating providers. So you have, they call it
near warming. It uses renewable energy. It is kind of cloud computing inside
the container. But this only means everything is automated. It's done by
two German companies. It's 100% open source and it runs totally
automatically. So the only problem if you want to drop these containers
everywhere into your local heating systems is security. So you have to
build the containers in a way that nobody has access or that it is turning
off everything if somebody gets access. But this is solvable. What did they
do? They have upcycled the heat to 85 degrees of Celsius, which is the
lower level where these heating systems accept the energy. It's scalable
between 50 kilowatts, so this is a small container, and 50 megawatt,
which is roundabout the same as NTT. It makes economic sense. They told me
beyond 250 kilowatts, this means five containers or something like this.
This means you have two gigawatt hours or the equivalent of 400 houses.
But the customers want at least 500 megawatt data centers. So if you
calculate this, this means 40 gigawatt hours or you have to have 8,000
houses or some kind of industrial usage for the heat. The good news is this
is not just science fiction because the real estate companies are
interested to build it into new housing solutions. So this seems to have
a value. Another more science fiction-like project is in the tropical
country of Finland. They simply have a sand battery for heat, so they heat
up sand in the summer or with whatever measure. Here, at the moment,
it's not really visible, they directly transform energy into heat into
the sand. This means you could also include some CPU computing in the
middle of this process that you get a better usage of the energy. So this
is a waste of energy in the term of, yes, it could also compute something
in between. It's called the Hot Heart Project. What we see here, it's
quite complicated. We have seen this with heating discussion in Germany
where people even triggered a campaign which was more or less a psyops
campaign against the green transformation of heating houses. So this
is more complex and this will take definitely longer. So in my conclusion,
what we can think about, and it is a process that will take more or less
10 years, 20 years or 30 years, we should integrate data centres into the
infrastructure with a PoE of one and use the heat. And the Gazak told
definitely, it's probably not the perfect solution we have, but it's
better to start now than wait for the perfect solution because the
perfect solution could look different on every location. There's a lot of
pressure behind because otherwise we don't get our decarbonisation goals
and this is really something we should not miss. So this was more or less
an overview. I think it was not sufficient in that term that you have
now a plan to do, but I think I wanted to raise at least that we have a
problem here and that there are people starting to have solutions, but
definitely not final. So if you want to contact me, here is my contact
data. And now we are open for Q&A.
So thank you so much, Thomas, for giving us all this knowledge. Over
there is an angel. If you have a question, please go over to the angel.
You can just queue with the angel and the microphone. Okay. Do we find a
way to do this? Wow. Good that we stopped early. So as we only have like
30 minutes for questions, maybe not more people queue.
>> Okay. >> Yeah. I just read this week an article
about the Google data centre plant in Brandenburg and they said they will
not really need much water because like most big new data centres, they
have closed water cycles combined with air cooling and then they just
don't need that big water consumption. So what is your perspective on
this? >> Normally this is a POE value, so normally
you can get a good POE only with water. And you can exchange --
>> It's still a water cooling system, but it's a closed system. So you
need to fill it once, but not all of the time.
>> But then it's not as efficient as they would simply vaporize the
water. It's simply -- if it is closed, it's better than air cooling.
>> We have this -- >> But you can reach a POE of 1.2, that's
possible? >> It's possible. And Google has done it,
but at the moment only with a lot of water consumption. And if they
can be better, I think they are happy if they are below this 1.2, but
they will not go down to 1.03 or something like this. So this is an
improvement, but it's not the thing they could have if they would
waste energy. >> I appreciate that there are smaller options,
because I also don't believe in the giant solutions. One last
sentence. We can reuse heat also for other things, not just for
warming houses. >> Exactly.
>> We can dry food and agriculture, we can have greenhouses to grow
stuff in the middle of nowhere like Mittenwalde. So there are other
options, too, but it's a disaster that the law has been -- has been
destroyed to its core. >> The problem is then every solution is
individual. So you cannot simply have -- yeah, we use heat for one
data center and scale it and run it everywhere. You have the problem
that it has to be designed locally, which can also be an advantage
for the local industry, but there is no single --
>> And if you force them by law, they have to find a solution, and
now they don't have to anymore. >> Yeah. The law is --
>> So maybe short questions and short answers so everyone can ask
their question. >> Okay. My question is in the same direction.
Isn't there a law which forbids to use fresh water with all the
dirt grouts and water -- >> Not at the moment. So you need a
license like Tesla in Brandenburg or like brown coal in -- but then
if you have this license, you can do whatever you want with the
local water. >> Hey, thanks for the talk. A question about the
software side of things. So as you mentioned, there's a lot of
opportunities to -- especially when dimensioning a system to
reduce the size and therefore reduce the consumption and make
better use of the resources that we already have in those data
centers. Do you think that working on the foundational
technologies, so for example building more efficient compilers
with smaller software closures that end up in all of our Docker
containers would make an actual impact in the long term?
>> All this helps. Even better CPUs would help, and you have to
look into it. For example, there are a lot of comparisons that
Python is inefficient, which is not true, because Python is
mostly a wrapper around good old C or Fortran libraries. So you
have to look into your application and measure the energy. And
effectively, you would need somebody in your team who is aware
of this and can scale the energy load down. This is something you
have to learn. It's not your daily business probably as a
developer, but somebody should have this knowledge in your team
or you should have somebody. This would be my proposal.
>> Take it as a small suggestion for people interested. Take up
open source projects, especially the ones that everyone uses, and
try to make small improvements there. You can have a huge
impact in this terms.
>> Yeah.
>> Hey, thanks for the talk. Really interesting, super cool.
Very naive question. So one of the things that we can actually
do with the increasing computing demand that we will have is
recapturing the energy in terms of heat. That's great. But very
naive question. Do we know what the fraction of low energy use of
our society is? So, you know, heat compared to electricity?
>> Oh, that's a good question. I have no numbers for that. But I
think it depends on the climate and the local situation. In
the isolation of the housing, so Germany is very bad on that.
Scandinavia is, because it's in a tropical area, obviously is
much better on that. So we have to improve on all levels. So
isolation, heat usage, better effective data center, something
like this. Otherwise it would not work.
>> As soon as we have to increase the exergy again by
converting heat back to electricity, it becomes really
inefficient. So, I mean, it's better than wasting it, of
course. It's free in that sense. But this is something that I
think we should look at.
>> Yeah. So it's not very efficient to transform this kind
of energy in electricity again. This is thermodynamics, yes.
Entropy is, heat is entropy on -- thank you.
>> Hi. Thanks for the amazing and much needed talk. I was
wondering, you identified the main drivers for electricity
consumption in data centers being compute power. I was
wondering if storage also generates usage.
>> Yes, it does. But it's proportional. So you have more
or less storage network is some proportional. And I did not
want to overload the talk with all these details. There are
details somewhere in the Internet. But, yeah, it also
contributes.
>> Hi. Thanks for the great talk. Are there any conflicts
expected between Mittenwalde and Grünheide, Tesla? Because
Tesla is also --
>> Tesla is also consumer. I think the real conflict is
between coal and Tesla. The coal consumption in the brown coal
region is much higher. The water consumption is much higher.
This has also an impact if they stop using water. In Berlin,
the river Spree will go down 75 percent, which is more or less
a catastrophe for Kreuzberg and southern Berlin.
>> Is it right to say the cooling is the most important and
most inefficient point on data center energy consumption?
>> It's one of the points because it does not compute
anything useful. So if you waste it, it's gone. If you can use
it, you have a saving of some percent. But if you can use the
entire CPU energy, then you have a major progress on saving
energy.
>> If we solve the cooling problem, we are very much
better. I mean, we can see in the comparison of Google and
Microsoft, there are different approaches to cool. And this is
the point on that we can work technically. And I'm working on
absorption cooling using this very low caloric heat. And
the temperatures are about, I don't know, 50, 60 degree
maximum. And this is something you cannot use in many ways.
Normally, you can use waste heat. And that's why I want to
ask you how you would say this kind of absorption and
absorption technologies are on the board, you know, to solve
this problem on a big scale.
>> This is the idea of this people. They have a heat
exchange. Normally, you get 35 degrees.
>> It's a heat pump.
>> It's a heat pump, yes. And this is a lower threshold for
being useful for warming houses.
>> Yeah, but what we do is we use the heat for cooling in a
thermal cooling technology.
>> If you can do that.
>> Yeah, this is thermal cooling. It's not the compression
cooling. It's another kind of process. You don't need
electricity. You use the heat to cool. The COP is 0.5. From
2 kilowatts heat, you can get 1 kilowatt cooling. And this is
what I think is a very good idea. But the -- yeah, I mean,
the mainstream in this question does not see this solution in
a way I would expect it.
>> I would look to dive deeper into that. But I have not seen
that so far. It's definitely interesting.
>> All right. So are you -- do you want to ask a question?
>> Yes.
>> Yeah. Still. Two minutes.
>> Actually two questions. One is if you use the heat for
heating houses, I always wonder what happens in the eight,
nine months maybe in Europe where you don't heat the
houses. What do you do? These numbers seem theoretically.
>> Yeah. So ideally you save the heat somewhere in the sand
and then consume it in the winter. So this is a Scandinavian
solution. And this means that you need a certain scale, that
you need a certain environment, and you have to rethink the
solution carefully. So if Finland can do it, probably we also
can do it. But at the end, you have to consider every aspect
of the environment, the local situation, and so on.
>> And question number two is PUE is very much focused on
hardware, right? I guess because they can measure the energy.
But as you said, you can save a lot of power by programming
more efficiently. Like you had the example 10 H-base servers
versus one. Do you know of any measurement or metric that I
can advertise my software for being efficient with?
>> So for Kubernetes, Microsoft has a framework, but I have not
looked deeper into it. So you can measure it. But you have to
also take into account that the business model of the data
centers is to sell you more computing power. So they are not
interested in you saving energy. They are interested in you
wasting energy. So this is some -- this goes into the opposite
direction. And I have no idea about an incentive, how we get
this together, that the data center providers help you to
save energy.
>> Thanks for the amazing talk.
>> Is it a short question?
>> It's super short. It's just one thought on this. If they
don't have any interest in defining something to measure
this, we should.
>> Yes. Because this -- effectively, this is your money.
This is cost. This is directly proportional to the money you
spend to the clouds.
>> Good. Then thank you all for coming. Thank you, Thomas, for
answering so many questions.
[ Applause ]
[ Music ]
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[ Music ]
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