{"id":901,"date":"2021-05-03T08:30:45","date_gmt":"2021-05-03T07:30:45","guid":{"rendered":"https:\/\/devblogs.microsoft.com\/sustainable-software\/?p=901"},"modified":"2021-05-03T18:37:53","modified_gmt":"2021-05-03T17:37:53","slug":"carbon-proxies-measuring-the-greenness-of-your-application","status":"publish","type":"post","link":"https:\/\/devblogs.microsoft.com\/sustainable-software\/carbon-proxies-measuring-the-greenness-of-your-application\/","title":{"rendered":"Carbon proxies: Measuring the greenness of your application"},"content":{"rendered":"
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How environmentally-friendly are your applications? How can you know?<\/h3>\n<\/div>\n
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Earlier this week, my smart meter finally arrived and it changed everything. Now, I get real-time metrics regarding my electricity and gas usage displayed on this little device which I have placed next to my laptop. We’re currently consuming 473W, which is above average \u2013\u00a0but<\/strong>\u00a0for a family of 4, during a COVID-19 lockdown, with schools closed and everyone working from home during the middle of winter, it\u2019s not bad.<\/p>\n<\/div>\n

\"Smart<\/a><\/p>\n

I can see it jump to about 500W for 5 seconds when I raise my desk. Turning on all the lights in the house is about 250W, and running my ceiling fan seems to be a cool 3W. The underfloor heating is an eye-watering 4,000W when it switches on (it doesn’t switch on often!).<\/p>\n

I had also just gone throughout my house and added in smart heating with separate thermostats for each room. (In case you are wondering I use\u00a0TADO<\/a>\u00a0\u2013 it took a little bit of time to tune and set up, but I have been very impressed with it so far). Now, every day I stare at this number and think, \u2018How can I reduce it?\u2019. Every day I’m tweaking something, checking something; the engineer inside me has kicked into full gear and I’m constantly thinking about and analyzing how I can bring that number down.<\/p>\n

This is the power of metrics, if you can measure something, you’ll optimize it. How do we measure a green application?<\/p>\n

Measuring carbon is hard<\/h3>\n

I’m a green software engineer. (Read the\u00a0principles of green software engineering<\/a>\u00a0to find out more.) The first principle of green software engineering is to build applications that are\u00a0carbon-efficient<\/strong>. That means for each gram of carbon our application is responsible for emitting into the environment, we try to extract the most value from that gram as possible.<\/p>\n

So, in this context, the first metric you might think of is carbon. And although, yes,\u00a0 we do need to calculate the carbon emissions of our application at some level, it’s particularly hard to measure. There is no HTTP response header that returns the carbon cost of the request (maybe there should be?), so most of what we do is try to model. When we model, we make assumptions, and those assumptions might impact where we put efforts to optimize.<\/p>\n

For example, a common challenge is the carbon intensity of electricity. Electricity is dirty and it emits carbon in its creation \u2013 how much carbon is a number called carbon intensity? The global<\/strong>\u00a0average for carbon intensity produced in 2020 was 519g\/kWh, but each electricity grid in the world has a different number because each grid has a different mix of clean and dirty electricity sources. It also changes over time, so each minute in each region has a different carbon intensity. Which of these numbers is the right one to use when calculating your carbon metric?<\/p>\n

If you chose a global yearly average then if you try to save carbon by running your application in a region with cleaner electricity, that change won’t be reflected in your carbon metric.<\/p>\n

If you decide to include real-time regional carbon intensity numbers in your calculation, then imagine this scenario. You release a code change and your carbon metric goes up. Did your code change make the application worse or has the electricity become dirtier? In this scenario, the real-time carbon metric is a problem as it’s causing false positives.<\/p>\n

At the same time, perhaps you released a code change that shifted workloads to times and regions when the electricity was cleaner. If this was the case, then you\u2019d want your carbon metric to use real-time regional carbon intensity numbers to tell you your code change had a positive impact.<\/p>\n

It’s good to attempt to model and think of the overall carbon number of your application, but be aware of how that number is being calculated and what assumptions are being made.<\/p>\n

Look for carbon proxies<\/h3>\n

An easier solution is to look for\u00a0carbon proxies.<\/strong>\u00a0If you can somehow connect what you are measuring to carbon, then it’s a proxy. And optimizing for that metric is optimizing for carbon.<\/p>\n

For example, let\u2019s take a look at electricity. We burn fossil fuels to make electricity, meaning there is a connection between electricity and carbon. This makes electricity a\u00a0carbon proxy<\/strong>. Therefore, reducing the energy consumed by your application reduces the amount of carbon it\u2019s responsible for emitting.<\/p>\n

How to choose carbon proxy metrics<\/h3>\n

The metrics you choose will open the doors to optimizations as if you measure something, you will usually aim to optimize it, so\u00a0picking the right measurement criteria is key<\/strong>.<\/p>\n

Our goal as green software engineers is not to write reports, it’s to implement change. And we need information to know what these changes should be. When picking a carbon proxy, be less concerned about how accurately you can measure it, and more concerned about the directional information the proxy gives you. It’s ok if your units are off, and it’s ok if it’s not accurate. As long as the metric behaves in a way so that it gives us the right information to make the right choices, it’s good enough.<\/p>\n

What you choose to measure impacts what you will optimize, and because of the assumptions that need to be made when summarizing,\u00a0multiple metrics are better than one<\/strong>.<\/p>\n

Some metrics are easier to measure than others: pick what’s manageable at the start, add others as you refine your green software engineering methodology.<\/p>\n

What carbon proxy metrics should you choose for a website?<\/h3>\n

\"Front<\/a>Let\u2019s take a look at what metrics a website might need. We\u2019ll pick a reference architecture of a basic multi-page application (i.e. most websites). Every time a user clicks a link, the browser makes a request to the server, the server generates some HTML and returns it in the response which the browser then renders on the client machine.<\/p>\n

We have a frontend running on a client machine, some networking, and a backend running on some servers. We assume the application is not a bitcoin miner or a game \u2013 essentially once parsed and loaded in the browser, the application is not running constantly and taking up CPU.<\/p>\n

So, what are some good carbon proxy metrics we might choose to measure for this application?<\/p>\n

Frontend<\/h4>\n

For the frontend, the ideal carbon proxy would be the total energy used to render the webpage. There are tools to measure this, however, pulling that all together into an automated measurement system is not easy. Another carbon proxy we can then use is\u00a0time-to-interactive<\/strong><\/a>. This is the time it takes from the response being made to the page being rendered and usable on the client device. We\u2019ll make the assumption here that the energy used is proportional to the time, though this might not always be the case.<\/p>\n

A lower time-to-interactive implies an application that:<\/p>\n