This topic gathers projects that exemplify or help to provide green computing. Green software is engineered to reduce energy consumption, which considers factors like algorithmic and language efficiency, networking, storage footprint, compute requirements, and so forth. Some projects follow great green software practices that should be highlighted; others help the rest of the world greenify their own code. The projects collected here are a mix of both.
A repository showcasing my journey through the Green Digital Skills program, focused on understanding and reducing the environmental impact of digital technology.
This repository contains the tool presented at ACAT2024 (PoC) and at ICHEP2024. PoC presented at ISGC2023
Novel blockchain consensus mechanism replacing energy-intensive mining with productive federated learning. Miners collaborate to train AI models, with winners selected through democratic voting.
Research Project by DreamBrook Labs
Saas app for physalia
☁️ Cloud GPU platform for AI/ML workloads. Instant access to H100, A100, and RTX GPUs for training and deploying AI models.
Green Software Engineering Playbooks
Web app for Physalia SaaS
2017 student project website on Green Computing
This project is a REST API REST API for a ticketing system using Django Rest Framework (DRF)
The Green Computing Scheduler is a carbon-aware Kubernetes scheduler that helps organizations reduce their carbon footprint by intelligently scheduling workloads based on real-time carbon intensity data. By delaying non-urgent batch jobs until periods of lower carbon intensity, the scheduler can significantly reduce the carbon emissions associated
Carbon-aware scheduling and traffic management Kubernetes Operator. It dynamically delays and redirects requests based on real-time energy mix data, optimizing workloads for greener energy consumption.
A plugin named wall-e-impact that uses the Impact Framework to measure game applications and their carbon emissions.
An open-source architecture for AI data centers that use zero freshwater. This repo provides practical designs for replacing evaporative cooling with closed-loop immersion, heat-to-power recovery, and adaptive AI-based thermal control, reducing water usage by 100% and energy demand by up to 20%.Built through a cross-AI collaboration.
An ebpf module that exports energy statistics for each process
Monitoring power consumption and calculates CO2 emissions from Kubernetes containers
[FGCS] Code and data for the paper "Adaptive green cloud applications: Balancing emissions, revenue, and user experience through approximate computing"