GreenCompute

Problem & Motivation

Data centers, the backbone of the digital economy, consume vast amounts of energy and contribute significantly to global carbon emissions. With increasing pressure from regulatory bodies, stakeholders, and consumers to adopt sustainable practices, data center operators face challenges in monitoring and reducing their environmental impact. GreenCompute addresses this gap by offering a user-friendly, data-driven platform to track carbon emissions and provide actionable recommendations to optimize energy efficiency and minimize emissions.

Data Source & Data Science Approach

We utilize publicly available datasets, including:

• SPEC Power Database: Provides server power metrics for energy consumption analysis.
• LBNL Data Center Tools: Offers insights into energy efficiency and carbon estimates.
• SCI Guidance Project: Contains embodied carbon emissions data for various data center machines.

Our data science approach incorporates:

• Regression Models: Predict carbon footprints based on server configurations and energy usage.
• Time-Series Forecasting: Anticipate future energy consumption trends.
• RAG-Based Chatbot: Deliver tailored carbon reduction recommendations using indexed best practices.
• Clustering Algorithms: Identify optimization opportunities across server groups.

Evaluation

Our solution is evaluated through:

• Model Accuracy: Achieving a Mean Absolute Error (MAE) of <5% for regression models.
• Chatbot Metrics: Ensuring high scores in context precision, relevance, and faithfulness.
• User Feedback: Conducting surveys and pilot tests to assess usability and the feasibility of recommendations.

Key Learnings

• High-quality, standardized datasets are essential for accurate carbon tracking.
• Simplicity and actionable insights drive greater user adoption.
• Integrating advanced analytics with an intuitive UI enhances decision-making for sustainability.

Impact

• Empowers data centers to reduce carbon emissions, supporting global climate goals.
• Assists organizations in meeting ESG reporting requirements.
• Provides decision-makers with tools to balance sustainability with operational efficiency.

Acknowledgements

We extend our gratitude to our project advisors, UC Berkeley W210 course instructors, and the organizations providing access to datasets. Special thanks to the teams behind LBNL and SPEC for their invaluable resources.