Tagline: A very short description of the project MAX 8 WORDS
Abstract:
In today’s world, emissions from software are typically calculated after it's deployment. The run time operational parameters are used in these calculations and tools like Cloud carbon footprint, Impact framework use this data to arrive at operational and embodied emissions.
However, design choices made during the Requirements gathering/software design phases greatly impact these carbon outcomes . Since these choices are made to choose the architectural approach, they are today available as specifications or architectural documents and not much intelligence is available around helping us understand how these choices impact the final emissions values that happen much later.
The approach outlined in this project suggests utilizing Large Language Models (LLMs) to proactively predict emissions from design choices and thus helping the community build specifications, tools and enablers around deriving actionable information from these architecture artifacts.
By generating relevant observations and predicted values for these options, LLMs could inform the Impact framework, allowing for pre-operational emission estimation. This project aims to develop an observation schema and leverage AI to predict environmental outcomes, offering a novel way to integrate sustainability into software development.
Quote: Include made up quote from a business leader.
Audience:
System designers and architects who design software systems are the primary audience. While doing so, several technical design decisions are considered along with their alternate design options and the pros/cons of each of these choices are evaluated. By empowering them with carbon emission considerations for their design choices, they are equipped to making informed decisions.
ToC: How will this project support one or more of our ToC pillars
Green AI
Governance: Which working group(s) do you think should govern this project?
[ ] Community
[X] Open Source
[ ] Policy
[ ] Standards
Operating Model: Will this project operate based on:
[ ] Consensus - Goal is everyone agree to every change so we are speaking with one voice when the deliverable is released.
[x] Maintainers - The Project Leads listen to feedback and incorporate it back into the project if they see fit.
Problem:
Today no explicit guidance is available to evaluate the carbon impact of software design options. We do have A project being run called Green Software patterns and principles but it is mostly referred manually. This is a missed opportunity since design is perhaps the most critical stage when the overall operational carbon emissions will be influenced by the design choices that the architects make.
2.Once the system is operational, it is rarely possible to revert back to the design and make changes and also cost of such changes are high
Solution: Try to make this as detailed as possible. The topics given below are just suggestions; address them only if they are relevant to your problem:
Rough design and scenarios on the probable effects, if any.
The use of diagrams is encouraged to elucidate concepts.
Address any possible objections.
Explain how the solution solves each of the problems listed in the problem section.
Closure: How do we know that the project succeeded? This has to be measurable if possible. Make references to successor projects, if any.
FAQ: Add anything here that doesn't fit into the above sections. It can be blank, to begin with; as questions are asked and points clarified use this section to document those clarifications.
srini1978
commented
4 months ago
Predicting Environmental Impact in Software Design: Leveraging Large Language Models for Pre-Operational Emissions Estimation
Related issues or discussions:
https://github.com/Green-Software-Foundation/gaic/issues/6
Tagline: A very short description of the project MAX 8 WORDS
Abstract:
In today’s world, emissions from software are typically calculated after it's deployment. The run time operational parameters are used in these calculations and tools like Cloud carbon footprint, Impact framework use this data to arrive at operational and embodied emissions.
However, design choices made during the Requirements gathering/software design phases greatly impact these carbon outcomes . Since these choices are made to choose the architectural approach, they are today available as specifications or architectural documents and not much intelligence is available around helping us understand how these choices impact the final emissions values that happen much later.
The approach outlined in this project suggests utilizing Large Language Models (LLMs) to proactively predict emissions from design choices and thus helping the community build specifications, tools and enablers around deriving actionable information from these architecture artifacts.
By generating relevant observations and predicted values for these options, LLMs could inform the Impact framework, allowing for pre-operational emission estimation. This project aims to develop an observation schema and leverage AI to predict environmental outcomes, offering a novel way to integrate sustainability into software development.
Quote: Include made up quote from a business leader.
Audience:
System designers and architects who design software systems are the primary audience. While doing so, several technical design decisions are considered along with their alternate design options and the pros/cons of each of these choices are evaluated. By empowering them with carbon emission considerations for their design choices, they are equipped to making informed decisions.
ToC: How will this project support one or more of our ToC pillars Green AI
Governance: Which working group(s) do you think should govern this project?
Operating Model: Will this project operate based on:
Problem:
Solution: Try to make this as detailed as possible. The topics given below are just suggestions; address them only if they are relevant to your problem:
Closure: How do we know that the project succeeded? This has to be measurable if possible. Make references to successor projects, if any.
FAQ: Add anything here that doesn't fit into the above sections. It can be blank, to begin with; as questions are asked and points clarified use this section to document those clarifications.