Clover: Toward Sustainable AI with Carbon-Aware Machine Learning Inference Service
This paper presents a solution to the challenge of mitigating carbon emissions from hosting large-scale machine learning (ML) inference services. ML inference is critical to modern technology products, but it is also a significant contributor to carbon footprint. We introduce, Clover, a carbon-frien...
Main Authors: | Li, Baolin, Samsi, Siddharth, Gadepally, Vijay, Tiwari, Devesh |
---|---|
Other Authors: | Lincoln Laboratory |
Format: | Article |
Language: | English |
Published: |
ACM|The International Conference for High Performance Computing, Networking, Storage and Analysis
2023
|
Online Access: | https://hdl.handle.net/1721.1/153142 |
Similar Items
-
Kairos: Building Cost-Efficient Machine Learning Inference Systems with Heterogeneous Cloud Resources
by: Li, Baolin, et al.
Published: (2023) -
Toward Sustainable HPC: Carbon Footprint Estimation and Environmental Implications of HPC Systems
by: Li, Baolin, et al.
Published: (2023) -
Sustainable Supercomputing for AI: GPU Power Capping at HPC Scale
by: Zhao, Dan, et al.
Published: (2023) -
MISO: Exploiting Multi-Instance GPU Capability on Multi-Tenant GPU Clusters
by: Li, Baolin, et al.
Published: (2023) -
Ribbon: Cost-Effective and QoS-Aware Deep Learning Model Inference using a Diverse Pool of Cloud Computing Instances
by: Li, Baolin, et al.
Published: (2022)