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Episode transcript

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Alexey: Hello and welcome to the ºÚÁÏÃÅ Technology Podcast. My name is Alexey. I'm speaking to you from Santiago in Chile, and I'm going to be one of your hosts this time together with Ken Mugrage. Hello, Ken.

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Ken Mugrage: How are you? I'm good. Thanks. Hi, I'm Ken Mugrage. I'm in Seattle, Washington, one of your regular hosts.

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Alexey: This time we're going to talk a little bit about the cloud carbon footprint, and we have two guests with us this time. Eric Smith and Cameron Casher. Maybe you two could introduce yourselves to the hosts. Eric, how about you? Can you tell us a little bit about yourself?

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AriK Smith: Yes, for sure. My name is Arik Smith, currently based out of Cleveland, Ohio. I've been at ºÚÁÏÃÅ for three years, anniversary recently. During that time, I've been primarily working as a developer and consultant on our sustainability solutions team, where I was able to help develop and currently maintain the Cloud Carbon Footprint project. As of now, I am still doing sustainability stuff, but also on our enterprise modernization and platforms and cloud team, where I essentially serve as a cloud specialist and sustainability SME on that team. Thanks for having me.

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Alexey: Thanks a lot, Eric. Great pleasure to have you with us this time. How about you, Cameron?

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Cameron Casher: Hi, my name is Cameron Casher. I've been at ºÚÁÏÃÅ for almost five years. I live in Denver, Colorado, and I've worked pretty closely with Eric for at least two or three of those years, I'd say, doing sustainability work and maintaining cloud carbon footprint. I think that I objectively have the worst podcast voice out of the four of us here, I'll just say that!

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Alexey: That's great. That's great. Thank you so much. It's a great pleasure to have both of you this time with us. Maybe we can get started telling the listeners, what is the Cloud Carbon Footprint? Can you talk a little bit about what it is, what it does, and where it came from?

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Arik: Yes, for sure. The Cloud Carbon Footprint is essentially an open source project that we launched back in March of 2021. It's essentially a tool that allows you to measure and monitor your carbon emissions and energy usage from services in the cloud. It currently supports three major public cloud providers. Your Google Cloud, AWS, and Microsoft Azure. I always say three, but we also recently added support for Alibaba Cloud as well. That's our fourth one that's brewing and catching up with the others, so to speak.

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Essentially, we have a dashboard that allows you to visualize these estimations. It's very modular and you can customize it as well. Even though we have that dashboard, you're also able to stand up and consume an API implementation of it as well or a command line interface for it. I would say it's similar as far as running and deploying it to Spotify's backstage tool, where you stand it up within your own environment and we'll get into the methodology, but essentially connected to your cloud provider accounts and are able to view estimations for a given time range that way. I think the origin of it and why we created it is a fun story. Maybe I'll kick it off to Cam to speak more to that point.

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Cameron: Maintaining Cloud Carbon Footprint has been really awesome. We have a really great microsite that contains well thought out documentation and blog posts and basically how you can understand the ins and outs of how Cloud Carbon Footprint works from both the calculations and to the methodology. It's getting iterated upon for the last few years now so that's one of the awesome things that we'd like to say to the contributors is it's always open for feedback and anybody can help contribute to it.

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Alexey: That's amazing. That's amazing. Cameron, you touched on something that I would really like to ask you. It's as much a framework, a methodology, or a way to do the calculations as it is a tool. Maybe you can talk a little bit about that. How does it come with the estimates and where did it come from? How did you develop that as well? Because that's an important and complex part of that as far as I know, right?

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Cameron: Yes. Basically, when we open source launched Cloud Carbon Footprint or even before that really when we were trying to decide what to build and how to build it, we decided to come up with a tool that could measure your Cloud Carbon Footprint and there wasn't really much out there. We had been referencing Etsy's Cloud Jewels, which is actually a very foundational piece of the Cloud Carbon Footprint methodology.

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We refer to that on the website and basically just built on top of that. The idea of why there needs to be a methodology is because at the time the cloud providers or the major three did not provide the details for Cloud Carbon Footprint. We had to essentially get data from billing and usage reports and convert certain metrics that we're able to grab into energy and then from there convert that into carbon emissions.

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Needless to say, there needs to be some sort of calculation or methodology in place and there needs to be different calculations for different basically usage resources. I know we might dive into more technical details but that's a pretty high level of how we need to go about making these estimations.

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Ken: You mentioned that there weren't really other things out when the idea came about and we first started creating this tool. Since then, most of the major cloud providers have done something. I remember very early on, at least, the numbers really always didn't add up. Different providers look different ways and so forth. How did that shake out? What's it look like versus the other tools? Why would you use this over, if you're using AWS, why not just use AWS tools or should you now?

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Arik: Yes, I can tackle that one. Essentially, I think there's a lot of fundamental differences. Of course, as you mentioned, every single cloud provider essentially has its own methodology that's out there and its own way of doing these estimations. One major thing is the way we source our data. Cam mentioned that we have to go about gathering usage details to convert to energy, then to convert into carbon emissions.

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The way we source those usage details are directly from your cloud provider billing data since a lot of setups are essentially monitoring your usage or pay-as-you-go setups that we're able to get that granular level information pertaining to, one, how much hardware you use or how much resources you use, such as if you're running a virtual machine, the amount of compute hours you're using, and then the type of configuration that you have, which is the machine specifications in this case of what microarchitecture for the processor, how much memory you have configured, how much storage, all of that fun stuff.

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That's like the one major fundamental piece is how we source it, and that goes into the major difference of how frequent you can get these estimations. A lot of the cloud provider tools essentially show you at least a month or sometimes even a few months back as far as your estimations for your usage versus with CCF, we can do it as frequently as we have usage data available through the billing data, so we can show you estimations from that same day or from the prior day.

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We also offer different levels of grouping or granularity, so you can view your estimations at a daily level, or a weekly level, monthly, quarterly, or yearly, and a lot of cloud provider tools essentially default to that monthly granularity as well. We also provide energy alongside emissions because sometimes you may want to see your usage in terms of kilowatt hours. Some cloud provider tools default to just the emissions and metric tons or kilograms that you're emitting.

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There's fundamental differences there, and of course with our methodology being open source and transparent, that transparency and the methodologies can differ as well, but as far as recommendations go as to where we see our usage comparing to those cloud provider tools, we actually recommend you use them in tandem because at the end of the day, our goal was to give you the most holistic transparency in your usage and emissions, and we have done work, to your point, Ken, of getting our numbers as close to theirs as possible.

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As they iterate on their methodology, we try to iterate on ours. It's a good feedback loop of confirming whether we're too far off the mark and what makes sense, especially as we dive into the way they go about it, but of course, if you want the most holistic view, it's good to have CCF to show you that multi-cloud level usage in one dashboard, which we found that a lot of organizations, they don't tend to stick to one cloud provider so it's nice to have an apples-to-apples comparison, single methodology application across your entire infrastructure, no matter what CSP you're using but iIf you want to dive into granularity with a specific cloud provider of maybe using CCF to see estimations for one, and also using a second cloud provider to confirm those numbers.

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It's up to you based on your use case, but we never recommend one over the other. The more, the merrier in this case, especially since at least we hope the setup doesn't warrant too much work that would stray you away from it but there's also tons of other differences I can go into as far as the tool itself, which I mentioned how customizable it is, how the level of configurability you get with it, and a few other things that stand out as well. Those are like the main things that we like to call out.

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Alexey: Yes, amazing. The multi-cloud aspect and being able to compare that across the board under the same method is a great thing to me. I'm sold just on that one. Then, can you talk a little bit about examples? Who's using it or in which ways have you seen companies use CCF and what are they use? Any examples to share?

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Cameron: I can start that and then I'll throw it over to Eric because I think probably I have like a couple ones we could call out. We have a public case study actually with a company called Holaluz from Europe. They were actually one of the very first, I guess clients we had with CCF, where we were able to help them implement that into their own basically organization in AWS.

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It was a really great experience. It was the first time we were able to understand what sorts of specific user requirements might be needed, what problems we might run into as we're trying to get this app deployed, scalability issues that we could iterate over as we went to improve the application. Really for Holaluz, I think the idea was just to get something up and running just at a very base level so that they could have some measurement in place. I think as we grew with CCF, part of our thinking shifted to, how do we implement CCF for our clients, but then also help drive them and steer them in a direction where they can really make use of the data they're seeing.

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This really gets into this concept of green ops where you can work with sustainability data and use it and bring it into your cross-functional data to have metrics that actually matter and, generate reports, do visualizations and charts and make sense of them and a whole lot of other stuff. That was our first example really. I know that Arik actually was involved in the publishing of a new case study. Maybe I'll let you speak to that one.

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Arik: Yes, for sure. Thanks, Cam. We actually had a case study for a recent client that we've worked with, which is Ivan. They're essentially a data platform company that provisions a range of different cloud-based services to their customers. We have a case study that went live with them earlier this week actually. Essentially what we did with them is a testament to the question that you asked earlier, Alexey, which isn't the bread and butter of CCF the methodology, which we think it is.

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A lot of people mistake CCF as a SaaS product, but we emphasize that it's a tool, whether you're using the methodology to help accomplish your own goals or you're using any entry points to the tool itself like the API, CLI, to embed it in your current systems or to use those as is based on what we have set up out of the box. A good example of this is what Ivan wanted to do is they wanted to bring carbon emissions data and calculations to their customers as a way of displaying or making that data available through invoices or through their internal platform whenever they bill and show their usage information to their customers.

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What Ivan went is that we served as SMEs and advisors on that engagement where they were actually able to take CCF methodology and build a new Python-based tool that would essentially serve as an integration of CCF within their system. That way, whenever customers get billed or sent a report of their usage from the previous month, that they can also see their carbon estimates and their impact based on that usage within that same type of dashboard or report.

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I love telling this story because I think it's just, again, a testament to how customizable and flexible CCF is to where we really tried to hone in on the methodology just to make this type of work possible. We'll be speaking some more about this. We plan on releasing a white paper about it and have an upcoming webinar that we'll be sending invites out for soon and communications as well. If you're listening, feel free to keep an eye out for that and if timelines overlap, then definitely maybe rewatch the recordings or go back and view that.

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I think that's just another awesome example of just, again, ways that companies are using CCF, as Cam mentioned, from different spectrums of standing it up as is inside of your current environment and using CCS dashboard out of the box to building something completely new just based on the methodology of the tool itself.

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Ken: That actually brings up something that I think one of you brought up earlier, a little bit about how it's deployed. A lot of times these types of things are SaaS tools and you get what they gave you. You get whatever your dashboard looks like and you can do what they predicted and nothing else. I know that's not the deployment model for this. What is the deployment model? How does somebody get and use CCF? Do they have to write their own Python or can they download something?

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Arik: Yes, for sure. One thing going in and that we've emphasized throughout its entire development is that we wanted to give options. I mentioned Backstage as an example because if you ever stood up and maintained your own Backstage instance, the idea of CCF is the same in which you would clone the repository of CCF or create a new instance of the app via NPM script that we've made available. Then you would set up your environment variables and configurations to connect it to your appropriate accounts and point it to the correct billing table so it knows where to actually get your usage data from. Then how you go to deploy it is totally up to you.

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We have Helm charts and different configurations to allow you to stand up a Kubernetes cluster if you want to deploy it that way and scale it through that method. We have AWS CloudFormation templates if you want to easily deploy it to EC2 instances that way. We also have write-ups and blogs on how to stand it up manually in a virtual machine. We have, if you want to stand it up in a serverless option, such as like Google App Engine, we have templates and configurations already ready to go for that so all you have to do is download the gcloud CLI and just run the appropriate commands. It's pretty flexible.

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We also have Terraform scripts ready to go as well as a starting point for you to customize and deploy it that way. It's again, a combination from ways that we've deployed our own CCF instance through smaller sandbox or different organizations within ºÚÁÏÃÅ, things that community members have contributed and actually opened up PRs for that they've said, "Hey, I actually prefer to deploy CCF this way, so here's like an example script of how I did it and we make it available to the rest of our users or just things we've done with clients that we found was best for their environments, and with permission, of course, we were able to build back into the tool as another option.

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We try to take a very unopinionated approach in that regards because again you never know if someone is standing up a full CCF React dashboard and API in tandem with each other or just using CCF's API to connect it to their existing dashboard that they use for monitoring costs or usage or whatever they may have going on within their organization. We try to take a hands-off approach and hopefully offer as many options and resources to get you started in the way that you want.

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Alexey: Yes, it's interesting. you've been mentioning customization in several different ways. It's not just options in which we need to deploy, you can also use parts of the Office TCF in different ways and integrate that to an existing ecosystem. Maybe we can shift gears a little bit and then start getting into some of the more technical details. Can you talk a little bit about the overall architecture and then how does that lend to customization and to different users of TCF?

Arik: Yes. Cam, maybe you want to tackle this?

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Cameron: Yes, I can now. I can start. basically we've got CCF obviously living in a GitHub repository. It's a mono repo where we have, React front end dashboard, and then we're also using a node and express on the backend, and we have a number of different packages or basically different services trying to make it fairly modular where we have our core logic separated from logic where you need like specifically for connecting to AWS or GCP or Azure. We have different packages for different cloud providers. We also have different packages for connecting via the CLI or the API. We have different ways that users can interact with CCF and get the data.

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We basically have our front end or our UI so the dashboard that you can load up. Then we also actually have a demo that'll be connected to the API that CCF has exposed. There's a number of other ways you can get the data as well. Another package we added more recently was our on-prem package. We had data separated or logic separated rather to help calculate for on-prem estimations that we wanted to start trialing out as well.

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Hopefully, that helps from a high level architecture but basically another visual I can try and give you. I actually have a visual in my mind. It's an architecture diagram that Arik had created where it's sort of understanding the flow of the data. Basically when you use CCF, you're connecting all your cloud providers that you'd want and you hit the API that tells you what cloud provider you want to connect to, what time frame of data you want.

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You can even add even more filters than that but then you'll essentially grab that data from the usage or billing data from the cloud providers and then that data gets returned, and we essentially do the core logic that separates out the usage data into various categories, like compute, networking, storage, and memory. Then from there we go about creating our estimations that are returned in our API format. That's the pretty standard way that we use a CCF, but there's a number of other ways, like I mentioned, CLI which is very useful for some folks. Arik, feel free to chime in if you think I missed anything blaringly.

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Arik: No, I think you awesomely called out something that I wish I would have pointed out earlier, which is that on-premise piece and how we were able to leverage, CCS methodology to, essentially make estimating on-premise data centers and even anything granular, such as, laptops and desktop usage as well.

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As far as the data outputs, it's unique in the way which works and which essentially given our data model which we have added on our microsite and detailed documentation on, what information is needed for that, as well as a cool article on our blog, as far as how to, go about gathering the data for that, but you essentially use a CLI to provide a CSV input of all of the different on-premise machines that you wish to estimate, and then we use the same methodology and same data and coefficients that we use to see CCF to output a usage for each of those line items as a CSV output.

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I want to call that out because we mentioned dashboards, we mentioned API, but we support CSV outputs as well. In terminal tables. Even if you're using a dashboard, we support being able to export the data within a dashboard to PDFs or CSV files to share around your organization. Another cool piece I wanted to mention as well, which again is a testament to the customization and extensibility of the tool is that we've also added support in partnership with electricity maps as well to add support for marginal and real-time carbon intensity data, which is great because electricity maps has tons of cool data for carbon intensity of regions across the world.

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It offloads a lot of that work that we've been trying to keep them updated to the awesome work that they've already done, but also adds, again, some of that marginal carbon intensity data that we talked about in which you can include carbon-free energy percentages and incorporate renewable energy metrics relative to that region. You can also go ahead and of course get real-time data for a specific timestamp in that region. What the carbon intensity, number may be at this moment in time in New Zealand may be different than what you have two or three years ago for that same region.

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We're able to get the actual, accurate number for that as well which is really cool and something that we've been finding has been valuable for people that not only want that real-time monitoring, but also want that historical data collection and care about the accuracy for that. I just wanted to shout that out as well because I felt like that's an important part of the nitty gritty technical details and again, of how extensible CCF is, but that's pretty much hits it on the head.

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Alexey: Yes, that's really cool. Nice, nice, thanks for sharing. Maybe you mentioned the architecture diagram, we can link that to the show notes so people can have access to that and have a look at it if it helps as well. How about, the maintainer's life? What were the main challenges? From a technical perspective, there's a lot of innovation and I imagine lots of hard problems on the way. What were some of the challenges in doing and evolving CCF? Maybe some of the problems you have right now from a more of a technical perspective, would you care to share some of that?

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Cameron: A couple of things that come to mind for me are, I guess there's different levels of problems. There's problems that arise from working onour ºÚÁÏÃÅ team, like developing CCF and there's just problems that we face just trying to keep improving upon CCF. A problem just like on a team level is, we had some dedicated folks on ºÚÁÏÃÅ working pretty full time and we always tried to promote any other ThoughtWorkers to join and help out when they wanted to, but not necessarily in a dedicated manner, so usually a lot of folks on the beach.

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We had to really learn how to effectively onboard some people. Then also off board the same people, a couple of weeks later, if they got staffed and really keep that context going and make sure we were doing it effectively where we didn't feel like we were wasting time in that way. That was a little difficult to navigate, but we did get some really awesome contributions from some ThoughtWorkers who were interested to join.

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Something else that came to mind was, as we're developing CCF, we're very limited to ºÚÁÏÃÅ data. As we're trying to implement different cloud providers, we need ºÚÁÏÃÅ to use those cloud providers so that we can connect and see what the data looks like, see what scalability problems we might face. Scalability was especially difficult to work with because ºÚÁÏÃÅ doesn't have that much cloud usage. We're primarily consulting and not doing our own stuff, so we had to rely on contributors or partners to really understand scalability issues and also other cloud providers that might want to be implemented that ºÚÁÏÃÅ doesn't use.

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Those are just a couple to name a few, but I'm sure if I thought about it for a little bit longer, could have some more, but Arik, I'm sure you have some good stuff too.

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Arik: No, I'll piggyback off of that and definitely say scalability was one of the larger things. It goes in just when you're relating to Cam's first point about the difficulties of onboarding and offboarding, that this is just such a complex and relatively new type of domain that it took a lot of upscaling and learning and things that we are still learning every day as developers of the tool.

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When we're talking about whether it's getting contributors or engaging with the clients, we're trying to ramp them up on the amount of necessary knowledge needed for this tool because now you've got to learn micro architectures of processors and to all the different type of services across cloud providers and the nuances across all of the different. CSPs of how a virtual machine and ways you go about gathering usage on Azure may not be the same way that you go about doing it on Google Cloud and et cetera. Those are definitely one of the main challenges. Since we're taking this multi-cloud approach, we try to practice keeping all the different-- I guess the proper term is feature parity across all the different cloud providers. Just out of fairness, we don't want to give edge to the other one to be able to support that multi-cloud model that we talked about. If we're developing a feature, we want to make sure that it works the same way or at least has its own implementation with the same result or similar result across all the cloud providers that we support.

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That's a challenge within itself because there's, again, so many different ways that a lot of the different cloud providers operate that there's a lot of gotchas when we're implementing one thing for a cloud provider and then trying to implement the same thing for another but the scalability is also a big thing as well because as Cam mentioned, we don't have as cloud workers, a lot of internal cloud usage outside of the few internal tools we have because we typically go to a client and work within their space.

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We've discovered that a lot of the problems like CCF has to work for the individual that is maybe building their first startup and wants to get ahead of their cloud usage as they're beginning, and also, for the large enterprise customer that has multiple applications with millions of users, if not billions, and is trying to make sure that they are getting ahold of their footprint for these apps that already work. Again, that could also go into the use case of a user that is trying to just get real-time feedback who only cares about seeing their carbon emissions for the same day, so that way they can remediate it versus someone that wants to actually track their historical usage for across a few years.

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That can range anywhere from, again, dealing with just a small subset of a small few rows of data to millions of rows of data that the tool now has to support. That was one of the big challenges we've come across and have been trying to tackle just in terms of adding support for MongoDB and their robust database and making sure that we're building out those flexible database options and supporting different deployment options as well that enables not only that individual user or that startup or that small to mid-sized company, but also the enterprise customer.

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Again, making sure our deployment options aren't just the naive one-and-done virtual machine approach, but also, again, can support Kubernetes clusters and more robust infrastructure and making sure again that we're flexible to all of those different use cases and thinking about the different spectrum of users that we may come across. I think those are like the main challenges we've been dealing with and have been adding hopefully cool new features to support.

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Ken: Especially like you mentioned, the people using it run such a gamut. What's the open source model like? A lot of times companies will open source something and they say, "Here's the source code," but they don't actually take any pull requests or that sort of thing. What's the model like? Do you take pull requests? How's the project managed? How do people get help with the code base? That sort of thing.

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Arik: Yes, for sure. We go off an Apache 2.0 License. By nature, anybody can of course clone and build off of CCF. Of course, there are some tools that have already done that, so some third-party tools that are out there that utilize CCS methodology as a way, or even build on top of one of the implementations of CCF within itself. We're pretty open. We take pull requests. As Cam mentioned, we're hosted on GitHub. CCF wouldn't be possible without the community of contributors and people who have provided feedback and have contributed to the tool over the years. We encourage issues on GitHub, whether it be bugs or feature improvements, or just even general praise, if you would like to do that as well.

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We're pretty flexible. We've had organizations come and as they're standing up, CCF may contribute back a pull request that has helped them out in their implementation that they want to build back into the tool. I would say pretty much just standard open source model, but we definitely welcome contributors that can be anywhere from making methodology changes to actually building out a full feature for CCF, which is where Alibaba Cloud came from, was the actual contribution to the code base itself.

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Alexey: We're getting to the end of the episode. Maybe, perhaps if you have any final comments or any final thoughts you want to share, I don't know, maybe a personal learning that you had contributing to CCF and helping build it, something cool you have learned personally, or some other thoughts that you want to share with our listeners.

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Arik: I'm so sorry. I did have a comment I wanted to add to the last question because I left off two very important points. Our project and overall issues and roadmap are all public within GitHub as a project board. You're welcome to view that and comment and add support for those roadmaps. We tend to prioritize our work based on what we see from the community. If we see there's a feature that the community really, really wants, we adjust it to our roadmap accordingly. If it's something new, we'll add it to it. If it's something that the community deems is not important versus another, like we're always flexible and try to accommodate that.

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We try to make ourselves available as well. Just as far as getting help or asking questions and maybe wanting to get involved and become a contributor. Again, we're on GitHub through the discussion board or through conversations via issues, but we also have a Google Group that you're able to join, which you can view on our microsite and we'll provide a link for hopefully people to join where it's pretty active. You can ask questions and get help from not only the team itself, but also other users as well.

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We mentioned that we're partnering with organizations that wish to be regular contributors to CCF and so we're trying to build a more immediate way of communicating with us that isn't through the Google Group or GitHub. Currently we're working on a Slack that people can hopefully join and get even more direct access to us and more community conversations as well. Sorry, I just wanted to throw that in there because I didn't want that to go left unsaid. As far as your last question for last thoughts, I'll pass it to Cam.

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Cameron: I'll kind of echo Arik said one of the things about maintaining is having the visibility into who's really using it so we don't really know unless people reach out and let us know or at their name, the ADOPTERS.md. It really gives us a good sense of things. Even a lot of the questions you were asking us, what are some examples of people using it? What problems are they facing? These are all questions we really would love to know so that's always a plug I like to give.

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I just like calling out the fact that being an open source software maintainer is just a really cool experience too. It's afforded Arik and I opportunities to speak about it like this in a lot of different ways, get to meet really awesome people, get to face unique problems dealing with the community and have a sense for just other general open source software too and look upon it in a different perspective as a maintainer, like Backstage, for example, "Oh, I wonder how these maintainers work on a day-to-day basis and what challenges they face. It's a really awesome art about being a developer for us so that's what I'd say.

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Alexey: Cool, cool, thank you. I guess this takes us to the end of the episode. Cameron, Arik, thank you so much for joining. It's been a pleasure. Thank you all so much for listening. See you next time, bye.

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Ken: Thank you.

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Arik: Thank you.