5 important things to know about your Cloud Carbon Footprint

5 important things to know about your Cloud Carbon Footprint is profiled by BTW Media because published evidence links it to internet infrastructure, governance, operational dependencies, or market visibility.

The Cloud Carbon Footprint is an open-source project to measure and monitor carbon emissions and energy usage from cloud services. The flexibility of deployment methods allows users to choose the approach that best fits their needs and infrastructure. There are 4 steps to measure CCF including gathering usage data, converting usage to energy consumption, estimating carbon emissions, and utilising CCF tools and dashboards. The Cloud Carbon Footprint is an open-source project launched in March 2021, which is designed to measure and monitor carbon emissions and energy usage from cloud services.

The tool offers a dashboard for visualising these estimations, which is customisable and modular. 1. What is the Cloud Carbon Footprint? The Cloud Carbon Footprint project was created to provide transparency and actionable insights into the carbon emissions and energy usage of cloud services. Initially, it supported three major public cloud providers— Google Cloud, Amazon Web Services( AWS ), and Microsoft Azure —and has recently added support for Alibaba Cloud. Also read: Spanish startups accuse Microsoft of unfair competition in cloud services The tool offers a dashboard for visualising these estimations, which is customisable and modular.

Users can also access the data through an API implementation or a command line interface. Similar to Spotify’s backstage tool, Cloud Carbon Footprint can be deployed within an organisation’s environment to connect to cloud provider accounts and view estimations over a specified time range. 2. How to get and use CCF? To get and use Cloud Carbon Footprint (CCF), individuals have multiple options available to them. They can clone the CCF repository or create a new instance of the app using NPM scripts provided. Setting up environment variables and configurations to connect to their accounts and billing data is necessary.

Deployment options include using Helm charts for Kubernetes clusters, AWS CloudFormation public-source contexts for EC2 instances, manual setup in a virtual machine, Google App Engine for serverless deployment, and Terraform scripts for customisation. Also read: Microsoft signs deal with Swedish partner to capture carbon The flexibility of deployment methods allows users to choose the approach that best fits their needs and infrastructure. Thoughtworks has incorporated various deployment options based on their own experiences, contributions from community members, and client engagements.

The goal is to provide users with a range of resources and options to deploy CCF according to their preferences, whether it involves setting up a full React dashboard and API or integrating CCF’s API with existing monitoring tools. 3. 4 steps to measure CCF Gathering usage data The first step in measuring your cloud carbon footprint is to gather detailed data from your cloud usage. This involves collecting billing and usage reports from your cloud service providers, which detail the resources consumed, such as compute instances, storage, and data transfers. These reports provide the foundational data needed to estimate energy consumption.

Converting usage to energy consumption Once you have the raw usage data, the next step is to convert this data into energy consumption metrics. This is done by applying specific coefficients and benchmarks, such as those from Etsy’s Cloud Jewels, which estimate the energy usage per unit of cloud service consumed. This translation from abstract usage data to tangible energy consumption is crucial for further carbon emissions calculations. Estimating carbon emissions With energy consumption data in hand, the subsequent task is to estimate the associated carbon emissions.

This involves applying regional carbon intensity factors, which indicate the amount of CO2 emitted per unit of energy consumed. These factors vary by location due to differences in the energy mix used to generate electricity, such as coal, natural gas, or renewables. Utilising CCF tools and dashboards To streamline this complex process, the Cloud Carbon Footprint project offers practical tools. CCF provides a modular and customisable dashboard for visualising carbon footprint estimations.

Additionally, it supports API and command-line interface implementations, allowing organisations to integrate carbon tracking into their existing systems seamlessly. By deploying CCF within their environment, businesses can monitor and analyse their cloud carbon footprint in real time. 4. Distinctive features Arik explains that Cloud Carbon Footprint (CCF) differs fundamentally from other tools in various aspects. One key distinction is the sourcing of data directly from cloud provider billing information, allowing for more granular details on usage, such as hardware, compute hours, and configurations.

This enables CCF to provide more frequent estimations, ranging from daily to yearly, compared to many cloud provider tools that often default to monthly granularity. CCF also includes energy data alongside emissions, offering a more comprehensive view. The open-source and transparent methodology of CCF allows for comparisons with cloud provider tools, with the recommendation to use them together for a holistic view. Arik emphasizes the importance of having a multi-cloud level usage dashboard for organizations that use multiple cloud providers. 5.

Practical applications and examples Several companies have already implemented CCF to track and reduce their cloud carbon footprint. For instance, Holaluz, a European energy company, integrated CCF with its AWS infrastructure to identify specific user requirements and scalability issues. This collaboration emphasized the importance of having precise measurement tools in place. Another example is Ivan, a data platform company, which used CCF to incorporate carbon emissions data into its billing and reporting processes.

By developing a Python-based tool, Ivan was able to integrate CCF’s carbon estimates into their internal platforms, showcasing the flexibility and customization potential of CCF.