Reducing Carbon footprint of Computing Systems

The proposed research and experiment will consider several applications such as ML training, distributed big-data data- processing, and web-microservices to quantify their energy and carbon footprint .

ProjectID

NGI-ATLANTIC:OC4-336

Acronym

Additional Info

At Chalmers, PI Hassan has been focusing on de-bloating machine learning (ML) systems. The aim is to reduce the resource consumption of ML systems by removing unnecessary features and inefficiencies in ML codes and models using automated tools. At UMass, PI Shenoy and his colleagues have been funded by the NSF to design a Carbon First approach to (i) make carbon a first-class systems design goal and (ii) to decarbonize cloud computing through carbon-aware software optimizations. These two projects address complementary issues and will come together to experiment with making cloud software applications energy-efficient and zero-carbon.

Enduser Relevance

The research will yield design principles for making cloud platforms and applications sustainable and low carbon.

Contact

Assistant Professor Ahmed Ali-Eldin Hassan, (Chalmers tekniska högskola)

Endorsements

Not available yet

Disclaimer

This experiment is currently underway.

Country:  Sweden United States

NGI Project:  NGI atlantic

Status: Early research demo

Category: Data and machine learningMeasurement, monitoring, analysis and abuse handling