Draft, Project In Progress
| Term | Definition |
|---|---|
| Carbon Intensity | Carbon emitted per energy unit. |
| Grid-Based Carbon Intensity | All entities who operate on a shared electrical grid, share a common carbon intensity. |
| Marginal Carbon Intensity | The carbon intensity of the marginal power plant that supplies power when additional load added to the grid. |
| Carbon Aware | Adjusted behavior in response to the carbon intensity of consumption. |
| Carbon Delta | The difference in emissions between carbon aware and unaware actions. |
| Carbon Counterfactual | The carbon delta had the carbon aware action been different. |
| Demand Shifting | Selectively changing the time/location of a compute's execution, to a time/location where the energy demands are met by cleaner energy production, resulting in a lower grid-based carbon intensity. |
| Operational Emissions | Emissions explained by the energy consumption and grid-based carbon intensity measurement during times of operation. |
| Embodied Emissions | Carbon emissions resultant of creating the hardware, structural systems, maintanence, etc. (e.g. constructing a GPU or datacenter). |
Other definitions and methods for the Carbon Awareness found at: Green Software Foundation
To enable organizations to make smart decisions about their environmental impact and carbon footprint, we have created the Carbon Aware API to minimize the operational emissions of computational workflows. A few key features of the API are:
- Utilizes marginal carbon emission measurements from WattTime to map carbon intensity rates to data centers.
- Generates a retrospective carbon emission analysis
- Provides forecasted demand shifting suggestions
- Supplies regional carbon intensity information over different scopes
- Hosts a green region picker for workspace configuration.
Marginal Carbon Emissions: This grid-responsive metric with finer granularity than average emissions, allows for seasonality/diurnal trends captured in demand shifting (source).
Retrospective Analysis: Time series evaluation to assess the carbon emissions for a given energy profile. Also provides counterfactual analysis to expose the potential emissions of if the run had been shifted.
Demand Shifting Scheduler: Recommends data center and/or time which would yield a less carbon intensive run.
- Temporal: Identifies the window of minimum carbon intensity for a specified run duration within a chosen region from a 24-hour forecast.
- Geographic: Finds the region with the current lowest average carbon intensity for an immediate run of a specified duration. Can filters available regions by available SKU and migration laws for workspaces with protected data.
Regional Carbon Intensity: Provides the carbon intensity for each data center supported by a WattTime-tracked balancing authority. The possible scopes are historic intensities (time series for prior 24-hours, week, and month), real-time marginal intensity, and forecast (mean intensity for upcoming user-defined window).
Green Region Picker: Recommends the a data center to host a workspace based on expressed needs such as carbon efficiency, price, and latency.
For more information, please see the full tool description: Carbon Aware API Details
Carbon intensity data for case studies provided by: WattTime
September 2021
Details: Geographically and temporally shifting compute instances at scale would reduce carbon by X%
Results: Organizations can reduce their operational emissions by 48% by geographically shifting and 12% by temporally shifting ML computes.
For more information and detailed results, please see the full case study: Carbon Optimized Demand Shifting at Scale
October 2021
Details: Execute and progress cloud computes greater than 24 hours in duration during periods of low emission to reduce the carbon footprint by Y%
Results: 48-hour ML runs saw a mean of 0.5% change using standard temporal shifting, but controlling progression based on regional carbon intesity thresholds reduced operational emissions by 10%
For more information and detailed results, please see the full case study: Carbon Segmented Cloud Computes