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Cloud Carbon Footprint (CCF)

website·Indexed 35 days ago

https://www.cloudcarbonfootprint.org/

An open-source tool for estimating greenhouse gas emissions from cloud computing usage. It provides methodology and account setup guides for AWS, GCP, Azure, and Alibaba Cloud, as well as support for on-premise data. The documentation covers deployment via Google App Engine or Docker, CLI and API usage, carbon intensity emissions factors, and integration with Backstage plugins.

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What's inside Cloud Carbon Footprint

  1. Overview of Cloud Carbon Footprint

    Cloud Carbon Footprint is an open-source tool that provides visibility to measure, monitor, and reduce cloud carbon emissions. It converts cloud utilization into estimated energy usage and carbon emissions using best practice methodologies. The tool helps organizations track Scope 3 emissions and identify opportunities for cost and carbon reduction through rightsizing and deleting idle instances.

  2. Overview of Cloud Carbon Footprint

    Cloud Carbon Footprint is an open-source tool designed for organizations to measure, monitor, and reduce carbon emissions resulting from cloud computing. It supports connecting to multiple cloud providers to provide a comprehensive view of total cloud emissions.

  3. Overview of Cloud Carbon Footprint tool

    Cloud Carbon Footprint is an open-source tool that provides visibility to measure, monitor, and reduce cloud carbon emissions. It converts cloud utilization data into estimated energy usage and carbon emissions, including embodied emissions from manufacturing. The tool is designed to help organizations track Scope 3 emissions and meet sustainability targets through actions like rightsizing and deleting idle instances.

  4. Overview of Cloud Carbon Footprint

    Cloud Carbon Footprint is an open-source tool designed for organizations to measure, monitor, and reduce carbon emissions generated by their cloud infrastructure. It supports connecting to multiple cloud providers to provide a comprehensive view of total cloud emissions.

  5. Calculate energy estimates for general Cloud Compute

    Energy estimates for compute are calculated using a combination of processor-specific constants and usage variables. If specific processor micro-architecture is unknown, the application uses provider-wide averages/medians. If Avg vCPU Utilization is unavailable via API, it defaults to 50% (based on 2016 U.S. Data Center Energy Usage Report).

    Provider Default Constants (when micro-architecture is unknown):

    • AWS: Avg Min Watts: 0.74, Avg Max Watts: 3.5
    • GCP: Median Min Watts: 0.71, Median Max Watts: 4.26
    • Azure: Avg Min Watts: 0.78, Avg Max Watts: 3.76

  6. Resolve unsupported Azure usage types or unknown regions

    If the application logs a warning about an "Unexpected usage type" or an "unknown azure region", the data for those resources cannot be correctly assigned. To resolve this:

    1. Identify the missing type or region alias from the warning message.
    2. Add the missing entry to the corresponding file:
      • For usage types: packages/azure/src/lib/ConsumptionTypes.ts
      • For regions: packages/azure/src/lib/AzureRegions.ts
    3. Delete the estimates.cache.json file.
    4. Restart the application server.

  7. Retrieve Azure Virtual Machine optimization recommendations

    Cloud Carbon Footprint integrates with the Azure Advisor API to provide recommendations for Azure Virtual Machines, specifically filtering for the 'Cost' category. The tool identifies opportunities to lower energy consumption, CO2e emissions, and costs by focusing on two primary recommendation types: shutting down idle virtual machines and resizing virtual machines for optimization (referred to as 'Sku change').

  8. Integrate Cloud Carbon Footprint into Backstage

    The Cloud Carbon Footprint (CCF) plugin allows developers to visualize and understand the environmental impact of their cloud usage directly within the Backstage developer portal. The plugin is officially published on the Backstage Plugin Marketplace and consists of both frontend and backend components.

  9. Implement on-premise data collection using AWS Glue and S3

    For large to extremely large datasets, a cloud-native approach using AWS Glue and an S3 Data Lake is recommended. AWS Glue handles scheduled and interval-based run initialization, while S3 provides scalable storage. This architecture can be augmented with AWS Athena, RDS, and Lambda for data transformation and retrieval.

  10. Understand Carbon Intensity Emissions Factors in CCF

    Carbon Intensity Emissions Factors represent the amount of carbon dioxide equivalent (CO2e) emissions produced per unit of energy consumed. These values vary by energy source and geographical region. Cloud Carbon Footprint (CCF) uses these factors to estimate the emissions associated with cloud infrastructure energy consumption.

  11. Calculate energy estimates for general Cloud Compute

    Energy estimation for virtual machines is based on the processor micro-architecture. The calculation uses Min Watts and Max Watts (derived from the SPECPower database) and vCPU utilization. If actual average vCPU utilization is unavailable from provider APIs, a default fallback of 50% is used.

    When the specific micro-architecture is unknown, the following provider-wide averages/medians are used:

    • AWS: Average Min Watts: 0.74, Average Max Watts: 3.5
    • GCP: Median Min Watts: 0.71, Median Max Watts: 4.26
    • Azure: Average Min Watts: 0.78, Average Max Watts: 3.76

  12. Understand the Cloud Carbon Footprint (CCF) On-Premise Data Model

    The CCF CLI requires on-premise data to follow a predefined model to ensure accurate data import and manipulation. This model includes common hardware and location fields such as CPU cores, memory, and geographic location for each endpoint. Detailed methodology and the current custom data model can be found in the On-Premise section of the CCF documentation.