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
