The Challenges of Location Shifting Data

In this episode Asim Hussain is joined by guests Scott Chamberlin formerly of Microsoft and Henry Richardson of Watttime as they discuss how time-shifting, location-shifting, curtailment and other terms are important to Carbon Aware Computing. How can we build sustainable software that reduces the impact on the environment and how these decisions may just lie in the hands of the developers instead of the CSR teams.  

Asim Hussain: And one way I think about carbon awareness is actually I'm building software, which responds to natural cycles of the earth. And it connects me with nature in an indirect way, but it's one of the few ways you can connect with nature. I think in software. 

Hello and welcome to environment variables brought to you by the Green Software Foundation. In each episode, we discuss the latest news and events surrounding green software. On our show, you can expect candid conversations with top experts in their field who have a passion for how to reduce the greenhouse gas emissions of software. I'm your host, Asim Hussain, welcome to the Environment Variables Podcast.

We have an exciting episode today talking about carbon aware computing.

Scott Chamberlin: Hi, I'm Scott Chamberlain. I was previously at Microsoft leading some of the sustainability efforts in the windows organization. And the day, this podcast is airing. I'm actually starting a new role at Intel leading their software sustainability effort.

Henry Richardson: And I'm Henry Richardson. With Wattime we're a nonprofit really focused on making grid emissions, available to partners to achieve impact through load flexibility of sighting of renewables. And so we're really excited about the kind of expansion of capabilities in software to take advantage of flexibility in grid emissions.

Asim Hussain: Henry cause this, this, this is your, this is your bread and butter. This is your space. Do you think you can give a, go trying to explain, you know, carbon intensity in these concepts to the audience here.

Henry Richardson: Absolutely. One of the things that we spend a lot of time thinking about is how. Clean or dirty that the electric grid is. And what we mean by that is when you make a change on the electric grid by increasing or decreasing load, how does emissions change? So if you decide to schedule a compute load at a specific hour, a certain set of power plants will be responding to that change in load and they'll have an associated emissions.

And so you can see how by scheduling. Load updates or sorry, windows updates to specific times you could actually affect which power plants are operating. Ideally, we would be scheduling those two when there's excess solar, excess wind, which can happen pretty often in the great Plains. There's a lot of excess wind, a lot of excess solar in California, but you can also pick between coal and natural gas, if you can have that flexibility.

So. We measure the intensity, the electric grid, and then we make that information available the software and making it available to software as what we consider carbon awareness. So can the software take advantage of that? The time varying emissions, intensity of the electric grid and actually change when it trains machine learning loads, as Scott was saying updates, major pieces of software, can you run.

At different times because they're, they're run chronically or regular. So things like that. So we see lots of opportunities in software to be kind of carbon aware and take advantage of this, this flexible.

Asim Hussain: Yeah, I think there's lots of other examples. I mean, there's, there's other big, big examples of well as well from other organizations, but where they started to apply the ideas relate to, to a carbon Alanise I think broadly, it, it, if you have the kind of. Software workload, which can respond to a signal, not all software can, that's the chatter challenge.

Not all software can respond like this, but if you do have the kind of workload that can respond like this, and that's why the windows update is such a perfect type of workload, because it's something that, you know, you need to do at some point. But you can have a reasonable amount of flexibility over when, when that happens.

If you're, if you're, if I'm visiting a webpage, I don't have that. You know, I need that web. And in three seconds, the other kind of famous use case I've seen is a large, large scale implementation of this I've seen is, is the work that Google's done with their carbon way data centers, which I think is quite an interesting, like the work that Microsoft's done with the windows is, is, is, is on a device.

And then Scott you've told me before I keep on saying, I'm sure I get the number wrong. Is it 10 billion devices around the world? Use windows? Is it? What is it?

Scott Chamberlin: think the last public number is either 1.4, 1.5 billion devices use windmill point for 1.5 billion client devices use windows, and then there's a separate, separate staff for our servers and data. 

Asim Hussain: Well, so there's a, quite a few people in the world who now think is 10 billion, because I just threw that stat out so many times in the past, one of the things you're involved with at your time at Microsoft Scott was well windows, but specifically, and then an announcement that windows made recently.

Do you want to give a quick summary as to what that was.

Scott Chamberlin: sure. Totally. And again, this is in partnership. Wattime and Henry's organization as us in partnership with electricity map and the tomorrow organization. One of the first things that we did in windows was figure out how do we bring carbon awareness into the. Operating system, right? The operating system is responsible for scheduling tasks.

One of the things that does, and the question we had was if we had a CO2 intensity signal, could we number one? Change the behavior of the operating system in a way that was beneficial for the environment and had minimal user impact. And number two, would that have a significant impact in the emissions associated with the energy used by PCs around the world?

It was a PC focused saying not necessarily a server or a data center focus thing. I'm still, yeah. Recently in preview, Microsoft released the. First implementation of carbon aware, scheduling for windows update. And so windows update is essentially how windows applies new features for users. And there's this whole set of criteria which go into when is the optimal time to apply an update for a couple reasons.

Number one, in a lot of cases. It causes a reboot to happen on your machine, not all cases. And in some other cases, it requires CPU cycles requires a bunch of things to happen, perhaps to close that kind of stuff. So we added to that list of criteria, carbon awareness. And so if we can, you know, with this within a certain timeframe, Find a period of the day where we think that the CO2 intensity and the grade is going to be lower.

We're going to try to do the update during that time, rather than at a time of day, which might be optimal from the other criteria, point of view, but might have higher suit to intensity. So that's. Feature, that's just been released to windows preview. And in an upcoming version of windows 11 is available to windows insiders, not available to the general windows population yet.

And they're doing evaluation and testing of, of that feature at this point, that's the ability to shift across the time of day, which we would call time shifting. And then there's the ability to just shift appropriate workloads. Yeah. To the place where it's being run and that's what we would call it.

Location shifting right. In, in the, and there's two, I think, critical challenges with each, right? In, in time shifting, you have to have some ability to be able to move the load to a different period of time. And that's where you were kind of referring out. Some is like, Hey, when I'm using a webpage, I can't really move the processing of that visit to a different time of day.

So. When you, and you have to be able to sometimes predict when that is, because you have to, a lot of times you're scheduling into, you're always scheduling into the future, but you don't always have. Long period of time to look forward and wait for a real time signal. So sometimes you have to prep your workload and predict when that is.

There's some interesting ML and AI stuff that while time invest in to predict when that period would be, so you can get ready, your load can get ready and do it at that time. And, and that's one thing that is really important for time shifting now, location shifting. It also has to be appropriate workload, but in a different sense, many compute workload.

Art require huge amounts of data to be able to read in and data shifting is really hard. It's actually probably a it's something that would block a really large scale location shifting implementation. If you had a huge large data dependency on that. So things like training machine learning algorithms are pretty hard to.

Location shift. If you're not already geo distributing your data to multiple data centers around the world. And many people are, you know, I'm sure Google in a lot of senses, geo distributing their data to many data centers around the world. And then they shift to, they could, I don't know what they're doing.

They could shift their. Processing to those, you know, regional locations, which had lower carbon intensity at the appropriate period of time and, you know, say follow the sun or follow the wind around the world. As long as the data was already there.

Henry Richardson: And that doesn't speak to the political challenges of shifting data, which is like, you might have different jurisdictions, like you have specific rules and won't let the data outside of the boundaries. So they're not only technical challenges, but also geopolitical challenges. I would say.

Scott Chamberlin: Totally agree Henry. Yeah. 

Asim Hussain: Every time you try and have this conversation with anybody about location. Shifting that's the, the word data sovereignty just comes up almost immediately in the conversation and it's challenging. But then again, like within large countries, like the United States, there is still a lot of variability between east coast and west coast.

And the date, I believe the United States is one data sovereignty region and the same thing can, is it not? Um, she's shaking her head, Scott.

Scott Chamberlin: A lot of the data sovereignty laws, the privacy laws are being written by the States, today as though like say Illinois has a really strong one in California is really strong. it depends on the nature of the data, whether it actually falls within that data, sovereignty law, not all data is going to, there's a lot of data that's just generic and is not tied to individual privacy stuff.

And so that certainly wouldn't apply, but when you're doing, you know, Machine learning or algorithmics, or, you know, big data processing on things that are associated with users or have data privacy policies associated with their collection and use. yeah.

there's going to be even in the United States, a lot of times per state laws, you would have to comply by.

So again, it depends on the nature of the data about exactly. You have to consider when thinking about these kinds of things, there's a lot of things like in processing, you know, batch processing, a lot of these cloud concepts there start when you, when you think of a cloud native, you know, world, right.

There's a tons of cloud concepts that are really appropriate for. Both time shift and location shifting, you know, in NLS to me, you did a lot of work on, on batch processing. You know, there's the, the work that has been done both in Google and Microsoft on, on cargo or Kubernetes, like how do you build it into the infrastructure so that if you do have appropriate data, you can start to have the data center operating in a carbon aware way and that's, that's analogous, you know, like We in windows, On the client and data center, you have similar concepts, but are more operating on those cloud data workloads, which are very different than what the client workloads are like.

So.

Henry Richardson: We focused on a lot on the challenges. We were just surprised by seeing, by seeing how many people are actually figuring out how to navigate a lot of those. Like maybe they identified instead of, because data can be so. Maybe they identify two data centers that are in different regions and just have local copies of both of those so that they can pick when they train.

So they're not picking amongst the entire set of data centers, but a specific set or like the windows opportunity. I, would've never thought of updates as an opportunity for flexibility, but it's a huge, like you have up to a week of flexibility. Whereas a lot of the conversations we have are like, we need this job to be done by the morning.

We only have. 12 hours of flexibility, but so the more flexibility you have, the more savings potential you can achieve. So I think we talked to a lot of creative engineers who have identified opportunities within their very specific software to figure out how to make it.

Asim Hussain: And I think that's one of the exciting things about this space is that there's just a lack of knowledge. And this is kinda one of the things I believe for a while is if you pass on this knowledge to people, I mean, hopefully some people listen to this podcast now, or then have an idea regarding some aspects of their workload or something that they can maybe explore with, with carbon where computing one of the things I've always, there's always been a lot of interests.

You know, one of the things we do in the green software like movement is we, as you look at kind of various, you know, as you know, various touchpoints to reduce the emissions of software and carbon awareness is just one of them. There's always been a lot of interests. The interest comes from the fact that relative to the investment, the return is quite high.

It's not, this is not going to be the solution, the one solution and organization adopts to, to, to reduce all of their emissions. But relative to the investment you've got to put in, you know, the return is quite high. I think I've seen those even that there was a paper recently. I'm not too sure. Much adheres to what I've heard from people I know in this space, but it's talks about an upwards of up to about 30% emissions reductions from workloads.

Although I've heard kind of at a top start more about 10%. I don't know how kind of, what are your, have you, have you guys heard anything about this, about the potential improvements from, from the.

Scott Chamberlin: I think, well, I can't really reference the Microsoft saving specifically. I don't think they've released that information. I think what you can say is that it's highly dependent on the parameters of your problem. You're trying to solve like Kendra is referencing in terms of the amount of timeframe you have to be able to shift, or the amount of locations you have to choose from, for shifting and what the marginal emissions are in those locations.

Right? Like shifting from you know, coal to natural gas might have a certain percentage opportunities shifting from coal to. You know, a hundred percent renewable, like wind or solar is going to have a much different, and if you can completely shift or partially shift, you're going to have a bunch of different stuff.

I think almost every implementation is going to have a different upper bound at what the savings is and, you know, getting good at measuring that and identifying what that is, I think is if you were to like break down, if somebody's thinking. Building some time shifting or some locations shifting carbon awareness into their application.

Certainly in the number, one thing that any user, any developer would need to do is model their potential parameters that they're going to be con they're going to constrain their problem. You know, come up with some estimates. Like if I can S if I can move 5% of my workload within 24 hours in DC location.

I have this much potential savings. And then going back to ask them your previous point about the potential, like the cost versus benefit and the modeling, the work like there's, it costs this much development work to be able to do that versus this much savings and any developers are going to need to do.

Like modeling and that estimation before they go forward with an implementation, because I can certainly think of many problems, which might not benefit greatly, especially if they already are very small and the amount of emissions they're generating and it might not be worth the implementation for that.

You might focus on other things, but there's certainly ones that are, you know, generating a lot of missions. The, the attributes necessary, like the flexibility and time or location, the.

data dependency stuff we've already discussed. There's certainly problems that could. Great benefits in terms of implementation time shifting.

But again, the prereq for all of this is To model that out, understand what that potential is before implementing. And then if I can, I want to talk about one of the things you mentioned asked them in terms of the cost is a benefit. I think you're totally right. Like I, you know, in other places as some, I'm just, you haven't mentioned here, but you talk about software sustainability.

The first, if you were to like, create a. A classification of software sustainable, and you've done this previously and I've seen it. And the first branch and that is, you know, making carbon efficient applications. And then the other branch is making carbon aware applications. Right. And so those of us who are new to software sustainability might think of.

Efficient applications in the past, we might've talked about this performance engineering or improving the efficiency of your algorithms or stuff like that. Almost all of that is really hard. If it was easy, people have Buddha probably have done it already. Right. The nice thing about carbon awareness is that it's a different.

Way of thinking about your algorithms that are already running and it doesn't necessarily require you to reengineer your algorithms or to change the underlying. Implementation of your software. You're instead changing the scheduling about how that, how those underlying things work. And yes, I totally agree with you.

Like from a concert has been at Fort point of view in a lot of cases, the low-hanging fruit is in carbon awareness and software that I've seen.

Henry Richardson: To kind of build on that. Once you've identified a piece of software that could have flexibility, both spatially or temporally there kind of tend to be two big factors that drive the potential one is how variable is the, is the location that you're in. So is there a lot of variability in the admissions rate?

And can you take advantage of that with your flexibility and then comparing across regions? But then the second piece is how capable are you a forecasting that variability, because then, you know, can you take advantage of that variability by scheduling it? So do you have 24, 72 hour week long forecasts that you can begin to say, how well does that forecast match?

What's actually going on and can I take, can I use that forecast actually think about when to schedule? So the first step is really saying, what software do I have that can take advantage of flexibility? And then the next section is. Once I had that flexibility, is there an opportunity to actually reduce emissions with that flexibility?

So you need both of those pieces to really, to be successful. 

Scott Chamberlin: Yeah. And Henry, so, you know Wattime provides a forecast, correct me if I'm wrong, it's up to about 24 hours. Right. And 

Henry Richardson: We just extended it to 72, but yes. 

Scott Chamberlin: great. And do you have any stats that say how they speak to the accuracy of the forecast over certain periods of time and. Time parents start to be really unpredictable and it is, it like correlated with whether it is correlated with a bunch of other stuff.

That's becomes more unpredictable. The further out you look, you look 

Henry Richardson: Yeah, that's a, that's a really interesting question. We we've shifted away from an accuracy metric towards an efficacy. So, if you were to shift based on this signal or this forecast, the signal, how effective are you at reducing emissions? 

Scott Chamberlin: okay. 

Henry Richardson: And so if we get the magnitude a little bit wrong, but we get the rank order or the, the, the, the time, right.

That's much more important than the absolute magnitude, but that's just a training trick that we use on our backend, but it kind of can be represented as accuracy as well. But to answer your more, the deeper question of like, what characteristics do we see? We see that like solar dominated regions tend to.

Slightly easier to predict because solar is much more reliable when the sun's up. And if you have cloud cover, you'll be reasonably okay. When can be much more unpredictable. So wind dominated regions tend to have variability. That's hard to detect far ahead, but we might be say, we think that this hour is likely to have curtailment.

We might not know the exact five minute period when we're throwing away wind, but generally we can shift load to. The periods that are much more likely to see that high variability or low emissions period.

Asim Hussain: You just used the word curtailment and I feel like we need to educate people as to that's what that magic.

Henry Richardson: Yes. It's a very jargony, I apologize, but in the industry we often refer to when we throw away wind and solar, because there's an excess of it or where there's not enough capacity in the transmission system to, to move that wind or solar to other places as curtailment. And we're starting to see. Certain grids throw away wind and solar at kind of pretty prodigious rates.

California throws away quite a bit of it in the spring because there's an oversupply of solar because the sun is shining, but it's high temperatures haven't arrived yet. So we're not running air conditioning also in the great Plains. There can be a lot of wind at night, but low load periods. So there's an excess of wind, I believe even in the Pacific Northwest occasion.

In the spring that the same low load situation, when there's lots of wind and solar, they'll actually spill hydro over the dams and not generate with it because they have to release it. So you can see how, like, if we can take advantage of these opportunities through load flexibility with software, that's, that's an amazing opportunity.

We also talk about devices often, too. So smart devices, EVs, that type of thing can anything that has load flexibility. We're very focused on software in this conversation, but you can see how it could be other things. 

Scott Chamberlin: Right. And again, I think I was thinking about that very concept Henry, in terms of, you know, you have to think about software in a very broad sense. When you talk about the total opportunity here for only talking about PC software, the total opportunity is, you know, going to be limited by the number of.

PC's in the world as, you know, windows devices, Mac devices, you know, and throw in, obviously, you know, the mobile devices in the world, which kind of sip power, but, you know, we all need to think about the broader definition of software it's software running, you know, in our thermostats, even though it's driving, you know, Both, you know, energy, if you're, uh, you know, electric heat or electric heat pumps and stuff like that, or natural gas use, which doesn't have the same benefit of the time shifting, but it's software, that's running like, you know, you, my robot vacuum cleaner, that's sitting right here.

It's software, that's running, you know, almost everything, you know, Future how homes and businesses are being controlled by software and have differing abilities to take advantage of the topics of carbon awareness that we're talking about. Right. And so, you know, the IOT space is huge relative to the PC space.

When we typically think about when we think about software or the cloud space, when we think about the Microsoft software, but those are all software developers and they all. It a lot of sense have connected, you know, internet connections and can take advantage of some of these signals. And, you know, I think another area that, you know, we, we talked about, I don't have the ability to talk about too much, but we need to think about what, how does this take advantage of disconnected environments?

Not every, not every phone is connected at all times to the internet. And can you still do carbon awareness when you're disconnected? I think there's a lot of work to be done there. A huge impact or not. I don't know, but some of the modeling and stuff like that, it has seasonal variability as well, which might be able to be built in as a baseline.

If you don't have, you know, rich live internet connections on at all times. 

Henry Richardson: We've had some conversations with our partners. Like what about fall back schedules or. That's still to still do some scheduling that can maybe not perfectly identify that variability, but can still take advantage of some of the grid emissions, variability. And we've also had conversations around like how frequently should that device be connecting to the, to the kind of grid emission signal and making decisions based on that, because that has a, a carbon penalty to it as well.

Because every time you reach out, run the computation to decide when to schedule load, there's a, there's a carbon cost to that. And are you, you have to make it. The flexibility is achieving savings greater than the cost. 

Scott Chamberlin: Yeah, we ran into and out of a feature, another feature, I won't name the feature, but we did investigate a machine learning, you know, approach to reducing, you know, matter of power, use a windows. It turned out in that case. The amount of processing and power used for the processing to run the algorithm was greater than the potential savings for it.

So, yeah, you're touching on a really great point. Then that goes back to the point I was trying to make in terms of, you got to model all of this, but yeah, Henry, you totally got a model. The. New stuff you're writing as well to make sure you're not, you know, stripping out all the potential savings by the new code.

You're going to start running here. And hopefully you're looking at loads that are large enough that, you know, the amount of algorithmics and. You know, connections and, And services you need to ride to do time awareness is probably going to be much, much smaller for an appropriate workload that you're looking at.

But again, that's where the modeling and the measurement is super important to be in with.

Henry Richardson: And we've seen people scale that level. Like if it's a small workload, they'll just pull the forecast once, make a decision and then not check it. Or they can even do it every like three hours instead of every 15 minutes or something like that. So there's lots of ways of like adjusting the workload to the, to the job.

Asim Hussain: One thing I wanted to cover. I think, I think it's quite interesting to also cover the future because one of the things I think Henry you've mentioned to me and I think is quite important, is that everything that we're doing today, like if you talk about modeling something today, That's today's impact yet.

The world is actually when we're moving towards a future where more and more of the energy is coming from renewables. And therefore the impacts. If you were to build something today where your carbon, where workload has an impact of 10% in five years’ time, it might have an impact of 20% because the world's becoming kind of a lot more variable.

I mean, do you have any estimates of how that's, how that's gonna go in the future?

Henry Richardson: We definitely are seeing an acceleration of renewable deployment, which is increasing the variability of grids. I mean, historically. The electric utility is balancing authorities. Grid operators have always matched generation to demand. And I think we're shifting into a paradigm where we're going to have to be matching more of the demand to the variable generation coming from wind and solar.

And so as that variability increases, we're just seeing kind of dramatic increases in curtailment. Renewable deployment that enabled just much greater savings. You're kind of shifting from a world where you're occasionally trying to pick up that excess renewables to a world where you're trying to avoid the peaking fossil plants, which is just a much greater opportunity from an emission savings span, where you just move a load as far away from the peaks, instead of trying to find those trucks.

Asim Hussain: Can we just dig into that for a second? Cause I think that's quite, that's quite interesting because that's almost the opposite of curtailment because you, are you talking about peaker plants? They're so.

Henry Richardson: Exactly. So you could have a hundred percent renewable all the time, except for occasional periods where they have to turn on those really dirty peaker plants, whether they're fossil oil, fossil oil, fossil gas, or fossil coal, as you just want to avoid those periods at all costs. Instead of right now, we're saying that seeing occasional periods.

Where we're throwing away renewables, and you want to move as much load into those. So it's kind of like this expansion of opportunity, which is really.

Asim Hussain: Because because those, my understanding those peaker plants is, you know, the grids need the capability. The energy very, very, very fast. And they tend to be natural gas don't or some sort of gas. Cause you can just burn that quickly interrupted workloads, I think is what it is. It's interrupting. So not running something for five minutes could be as valuable as shifting your workload to another hour because you're avoiding the worst emissions.

Henry Richardson: Absolutely. And of course you want longer periods, the more flexible. Great of that opportunity. So there could be a two hour period in the afternoon where they have to bring those peakers on. And if you can avoid that, that can be really good. 

Scott Chamberlin: But, but talking to this variability, one question that comes up pretty frequently is the nature of different grids and the makeup. And especially when it comes to grids that are. Are more towards the grids. We're going to need say 30 years from now, which a lot of times, you know, has the peak and Henry you're more of an expert on this.

So, correct me if I'm wrong that the peak loads are going to be handled by in some of these. Nuclear. I think I take long-term ideally in some cases, and then the base load is going to be, you know, renewables for the most part. And so in those grids, you know, I, when I think of a couple regions today that we deal with Iceland, you know, typically we treat it mostly a hundred percent renewables.

France is another one because it has high investment nuclear. We kind of treat this hundred percent. Carp R zero carbon region. And again, Henry, you have to correct me. I'm wrong, those two regions, but it's because they have this nuclear renewable, you know, not the nuclear in Iceland, but in France, high Metro renewable.

And in that case, the carbon awareness is just kind of a flat clean signal. And so as the grid evolves to these things that are like zero carbon grids, like the techniques we're talking about, they don't have as much impact. 

Henry Richardson: So, this is a really interesting disconnect that we're seeing right now. And especially in the near future load, flexibility will have a lot of emissions savings potential because it'll be able to shift out of those dirty periods into the curtailment periods. But once we eventually attain those a hundred percent or near a hundred percent clean grids, the flexibility won't be saving emissions directly, but it will be enabling a hundred percent clean grid because will be following wind and.

And if we didn't have that flexibility, we would have to be fossil resources. So like it's an essential piece of a clean future grid, but it's going to be harder to quantify the benefit of it in that view. 

Scott Chamberlin: That's a great way of putting it Henry it's it's we get to clean grids faster. The more we have carbon awareness because carbon Alinea, or this allows us to maximize the use of our renewables. Whereas today we're already curtailing them. Right. I think that's, that's an excellent way of putting that. 

Henry Richardson: Exactly. And so it's, it's like a critical piece of that future grid without it. We wouldn't be able to obtain it as quickly as efficiently as, as chief.

Asim Hussain: It makes me realize I had like a, an epiphany moment a year or so ago when I, I realized the way this is interesting in computing, but just generally the way we consume electricity. Is based upon the way the energy grid was created. Like a lot of other things we do in our life, we flex based off of what's going outside in the world.

Like, I, I don't try and grow plants in winter and my garden because it's cold. Right? So we, we normally have this thing where we flex and we change what we do based off of the natural cycles of the earth. But because we've just been, had this thing called coal, which you could just burn whenever we wanted, we've not had to have.

That pressure in the rest of our world. And what renewables is bringing into the world right now is like, well look you, you can actually do I find it? I actually find it quite beautiful because oftentimes we're way disconnected from nature. And one way I think about carbon awareness is actually I'm building software, which responds to natural cycles of the earth.

And it connects me with nature in a, in a, in a kind of abstract, an indirect way, but it's one of the few ways you can connect with nature. I think in software,

Henry Richardson: It's a really good, interesting point about the electric grid in that it's very unique in the sense that it needs to be balanced instantaneously at all times. There's no flexibility in terms of timing. So if there's a demand on the electric grid that has to be met immediately, you can grow rice and store it in a silent.

Or it's an, a grain elevator for a while and then release it. The electric grid has to be instantaneous and until we have a lot more storage or pumped hydro, we're not going to have that flexibility. And so demand has to kind of follow supply much more closely.

Scott Chamberlin: I think it was in California, right. Where they're starting to look at in terms of increasing storage by starting to enable all this, the growing amounts of EVs in the world. Connecting those batteries and utilizing them as a bunch of local storage. So we have this like future that's way more complex in the sense that, you know, we're, we're drastically increasing the amount of storage.

Sometimes it's centralized storage. Sometimes it's this municipal storage. That's like, you know, you're, you're , and it's just can be a local buffer, not only for your house, but also for your driving. And you could charge that in a carbon aware way. And then. You know, we're adding municipal, solar, ratting, you know, utility, solar, adding all this.

And I think, you know, the grid gets way more complex, but like you're saying, ask them, I think it all slowly, you know, you get these new patterns, these new natural patterns that start to arise out of it. Yeah. The technology is going to play a really key role in all of this, how to, how to implement. 

Henry Richardson: I'll toss another bit of jargon in here, V2G vehicle degrades. So there's both smart charging of your vehicle, but there's also can that vehicle actually push power back to the grid at important periods? Whew. It's all software driven, even though it's living on hardware. One other question for you.

Awesome. Cause I, I think we're approaching the end here, which is. One of the things that we're excited about, this is that it puts the capabilities in the hands of developers to actually affect this. This is something that a developer can make a decision about actually make, may affect the software that they're working on and have a real emissions effect.

Is that something that you've explored with?

Asim Hussain: Oh, you talking about some of the projects that are working in the foundation, because we have one particular project, which is a carbon aware of soft SDK, the software development kit, which is a lot of what we're describing here. I mean, the logic is the same. Every single company wants to implement carbon awareness is pretty much just creating the same logic.

And so one of our projects is create the carbon where software development care, which is gonna enable people to enable developers to much more easily implement some of this logic and functionality. And I remember when I was having, we were having conversations with our team and kind of the same thing came up, which was I w w we started this with this idea that we're going to reduce the carbon emissions of software, but actually software drives everything.

So this SDK could be used. For, I have an E vehicle in my driveway. So it could, I could build something to leverage it and charge my Evie based off of, you know, some sort of signal or heating my house or something like that, because I think that's, that's one of the things we're seeing is if we can just make it easier for people to do this, then the more likely to, to, to implement a lot of these things.

And I'm seeing it, I'm seeing it implemented another place as well as lots of websites that, and I love the impact might not be so good. In terms of carbon savings, but impact's quite high in terms of making people aware of what the potential is. There's a really great magazine called Branch Magazine from one of our colleagues here, Chris, Chris Adams, and you know, the it's an online web magazine, which changes its behavior based off of electricity grids.

And so the images will disappear if it's, if it's high carbon intensity and replace it with text and words. And that's really good because everybody reading that is suddenly then aware of this entire concept, because people aren't aware of it in the.

Henry Richardson: One of the pieces that I think I really like about it too, is that a lot of this is being driven by the developers themselves. Not necessarily the sustainability to. Like the corporate sustainability team at these organizations, they're like the developers see an opportunity. They understand how the code works and can actually make a decision about how to drive emissions or that.

Scott Chamberlin: I a hundred percent agree with you. I mean, some of that is natural in the sense that the. Inability to step in. Most corporations are driven a lot of times to the supply chain organizations. And a lot of times that's because the measured CO2 impacts a lot of the majority of is sometimes coming from.

Supply chain, but I think the opportunity in terms of cost versus benefit on the software side, I think is it's, it's an area where we can change faster and have some initial impacts greater than some of the supply chain teams, which supply chain changes, which are our longer term kind of things. And to be clear, all of those, both sides of the thing are totally interleaved.

There's not a fine line between them. 

Asim Hussain: So I think that's all we've got time for today. So it was really wonderful conversation that we want. And just a final thought for me. I just want to give a shout out to an event that's happening in the middle of June. So the foundation has a summit, a global summit, which has been run over 20 locations around the world.

If you want to meet other like-minded people, people like us kind of thinking and talking about these topics. You know, come find us a confined your local event at summit.greensoftware.foundation.

Scott Chamberlin: I think the final thought is that, you know, having gone and tried to build carbon aware software, it was as long as you're making sure that you have the ability to measure and that you are. Actually doing the engineering that is going to have an impact it's actually super motivating to look at. And it's actually the technologies.

It's a rich, rich area of technology. And it's may seem intimidating when we add these new kind of terms about to, about how the grid operates. You have to think about a, yet another thing is software, but it's, it's really, once you get in there, the concepts are pretty straightforward and adjusting yourself.

Do this kind of stuff is actually not too hard. So I'd encourage folks to, to try it out at least. 

Henry Richardson: I think one of the things. About, as people are coming up with use cases that we never thought about. So scheduling windows updates, like we hadn't even considered that as a possibility. So people come up with very creative ideas, but with region shifting location, shifting that, that we would never have thought of.

And so we're always excited to see that kind of expanding possibilities for loaded flexing.

Asim Hussain: Thanks for listening to Environment Variables, all the resources for this podcast, including links to our guests and more about carbon where computing as well as the Green Software Foundation. The show description below. We hope you enjoyed the show and see you on the next one. Hey everyone. Thanks for listening.

Just a reminder to follow environment variables on apple podcasts, Spotify, Google podcasts, or wherever you get your podcasts. And please do leave a rating and review. If you like what we're doing, it helps other people discover the show. And of course we want more listeners to find out more about the Green Software Foundation, please visit green software.foundation.

Thanks again and see you in the next episode.
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