Hello everyone, I hope the audio works and the last rows, speak up, okay.
So welcome to measure what you manage, transparent energy consumption of cloud infrastructure.
So in the following 10 minutes, I will really quickly try to take you on our journey for
measuring and understanding energy consumption in a focused on cloud environments.
And yeah, I covered some challenges that we faced while mapping software workloads with
physical research usage.
And we know that today cloud and it's up to 4 or up to 6% of the world comes a mission
which is a lot and also we know that this will contribute in the near future even more.
So normally I asked, do we have any public or private cloud providers with us today?
And if so, I would like to ask if you know the ecological footprint of your cloud environment
for example, for the last year, we are not only talking about energy consumption.
So yeah, normally if I would say like if anyone does it already, my reply would be
wow, let's talk about after this presentation, but if not why.
And isn't it something that your consumers and also the consumers of your consumers would
probably like to know?
Yeah, one to make it even more concrete in 2027, globally I demand could be responsible for
2, 4.2 to 6.6 billion cubic meters of water withdrawal.
And that's 4 to 6 times Denmark's total annual water withdrawal, which is a lot.
I would argue that it's really important to determine the whole environmental impact
of distributed digital systems and the software running on them.
So when we look at the impacts actually, let's talk at holistically looking at the environment
impact that we face.
So there are various approaches and attempt to really systematically map these potential
impacts.
For example, the computer related, which includes the embodied and the operational,
which we talked about, or the session before talked about, and immediate application impact,
they can be optimized because it's something like transportation and electricity costs,
but more importantly, we also have the system level impact, right?
So the rebound affects, for example, by increasing the GHD emissions.
So the solution could actually be looking at the lifecycle assessment, which enables
us to actually look at the environmental impact of these digital services and record them
over the entire lifecycle.
Environmental impact categories, since I only have two minutes, I will quickly let you
refrew it.
So it's about the energy demand, global warming potential, the resources it costs, water,
consumption, electronic waste as well, and the pollutant effects.
Coming to our research project, I could itch it.
It's funded by was funded as a part of the Green Tech Innovation Competition of the German Federal
Ministry of Economics and Climate Action.
Here's a website as well with all of the information.
And our research was the goal of our research project is as said to assess the environmental
impact for distributed systems, but it's a cross cloud edge and antivisers.
I will talk mostly about cloud, but it's a bigger project because we consist of a community
of partners from the science, research and industry, backgrounds, so our desolate
remains, but also the Institute of Applied Ecology and the German Society of Informatics
Apart.
And me as well, so we have the open source business alliance, a general on profit that
operates and at work of companies and organizations developing, on building, using open
source software.
So, a quick look into what actually happens in Ecology, I try to method out as like a
workflow, so I want to really quickly just show you what we are like developing on the
major levels, so it's called the test bench, and then you can define with IAC, your infrastructure,
then digital twins get creative of these different infrastructure, so antivisers, cloud
infrastructure, etc, and a system and a test can be executed on these test bench, and
then we collect metrics and use the mythology proposed by the Institute of Applied Ecology.
They implemented, but it's using so-called energy profiles and regression models to combine
not only the usage phase, but also as explained there's a using the prior shown environmental
impact categories we focused on, so in the manufacturing and in the disposal phase, but also
the research and energy consumption of the workload while running, so in the usage phase.
Yeah, this would be how we actually, but as said limited time, so maybe make a picture and
it's also online as well, but that's actually how we get the data, and so it's actually
like static energy profiles, and as a result, to move on further, you could define how
an actual infrastructure combining of different platforms contribute in its complete life cycle.
And for cloud, obviously, energy consumption is the most important aspect for us, I mean,
life cycle as well, but getting this information is really hard.
It's even harder.
I actually also want to look at the hyperscalers, because their access to relevant data is highly
limited.
I researched a lot, so I actually, so there's no exposure of MSR, and there are some solutions
like the Buddhist API and how they do it, and for example, there's also the cloud comfort
print, which is also an open source community, I'm using the billing APIs of the hyperscaler,
but what's very limited in these, what's very limited is still the information on the
management overhead, but that's a lot on hyperscalers.
So yeah, so to create energy profiles, we use the really, I'm similar way, it was first
introduced by Feds, and now it's part of the board, this API, but this, yeah, actually,
you look at the bare metal, you do stress, you collect certain rapid metrics, line them
with the utilization of certain processes, and then you use an instance, number of virtual
CPU ratio, and compare to the bare metal, number of virtual CPUs, that's really quickly
how an energy model is, I created.
So this is rather an estimation, and you use regression to estimate the actual usage after
deploying it as a digital twin, but how nice would it be to use an open cloud, where you
can actually access host levels, and find out what is the scaling effects, what is the
baseline of the management service, and how does it contribute to the actual workload?
This is what we did with the help of the Sering CloudStack, it's based on OpenStack for
the infrastructure as a service component, and yeah, so with our cloud provider, a technical
partner's cluster, and scale up, we build two different cloud environments, and then
we use sensors, so power distribution units, to get the physical actual measurements.
So in order that we can at some point really see how accurate is, for example, a solution,
a few of you might maybe know, Skafandra, or maybe in Kepler, MSR, metrics, how accurate
are they when you take the physical measurement as a baseline, maybe you need some sort
of distribution factor or something like that.
So yeah, we faced a few issues, starting off with two different environments, one is based
on OCP hardware, meaning you don't really have the option of having energy measurements,
per device, you only can measure it correct, so you need a distribution factor of how much
which serve us or note actually contributes, so we needed to make a concept that works
for different infrastructure, which can be a very heterogeneous, and yeah, that was the
goal, so we can really estimate the baseline of the management services, and then as
well, the workload running on it, I need to skip it, so it's just time-wise, but this
is, I actually got this graph from because it explains it really well what we are doing,
but it's from the Kepler project as well, so we are trying to find out we have a stressor
which runs a different number of virtual machines with the different flavors, we can estimate
it inside these machines, we have stress on G, and other tools to collect these metrics,
and quickly jump into this, we have the physical measurements with different
solutions, where as Rappel is the one we really want to look at how accurate it is,
so we focus on physical, actual physical measurements and allocations, and for the
software based measurements and for the software in CloudStack, for me, it has higher
availability status already implemented, so we use a lot of different exporters for all
the process, container and virtual machine levels, where we actually just use Kefanga,
which is running really nicely, but also I cannot, like, with 100% of confidence tell you
if these numbers are accurate, and that's the goal of what we are trying to do, so
and then to the on-time, the next steps, we still need to work on the data isolation and
the really payment of the allocation rules, and we need to integrate the runtime monitoring,
because I explain we have the static energy profiles, which we can already create, but the
question is, when you have a running, a re-production cloud, how would these static energy
profiles change, so this is why we need it, and now it's still working on it, and in a
reporting functionality at the moment is only implemented as Kefanga dashboards, but we
want to have it as a service, as a standard for the software in CloudStack, okay, this
was 10 minutes, but very fast, but thank you any questions.
How do you measure the power of the use for cooling the data set?
For again, sorry.
Yeah.
Who were your data set there?
Yeah.
How do you measure it?
Yeah.
This is why we have this technology partners closer than scale up, because I actually, I'm
not very, I don't have a deep knowledge of how data centers are actually what they are
already measuring, and I thought they had, like, very great, they had already solution
implemented to really have maybe some sort of exporter to get these information, the cooling,
but allocated to this one rack, because they have 100 from racks, different cloud platforms,
they have private cloud, but also their own public cloud.
And in our rack, for example, a few service of a different cloud environment, so how can you
allocate the cooling data, which they collect, specifically, this cloud environment?
That's a great question, and I'm still working with the cloud providers to completely
answer it, but at the moment, it's more like we collect the bits of materials with the
information on the, like it's this, that you could say is static approach, they fill out,
like, how much cooling energy cost and, like, they give us the details, and we can then use
some sort of allocation, okay, you have certain amount of cloud environments, our has this
size, and then we try to allocate it, a great solution we don't already have, but it's a really,
good point, it's still in the research.
Thank you.