Kairos: Building Cost-Efficient Machine Learning Inference Systems with Heterogeneous Cloud Resources-Reference-Cited by-同舟云学术

Kairos: Building Cost-Efficient Machine Learning Inference Systems with Heterogeneous Cloud Resources

Published:2023-08-07 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 32nd International Symposium on High-Performance Parallel and Distributed Computing
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Author:

Li Baolin¹^ORCID,Samsi Siddharth²^ORCID,Gadepally Vijay²^ORCID,Tiwari Devesh¹^ORCID

Affiliation:

1. Northeastern University, Boston, MA, USA

2. MIT Lincoln Laboratory, Lexington, MA, USA

Funder

United States Air Force Research Laboratory

Assistant Secretary of Defense for Research and Engineering

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3588195.3592997

Reference79 articles.

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2. The Architectural Implications of Facebook's DNN-Based Personalized Recommendation

3. Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective

4. Daniel Crankshaw , XinWang, Guilio Zhou , Michael J Franklin , Joseph E Gonzalez , and Ion Stoica . Clipper: A low-latency online prediction serving system . In 14th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 17) , pages 613 -- 627 , 2017 . Daniel Crankshaw, XinWang, Guilio Zhou, Michael J Franklin, Joseph E Gonzalez, and Ion Stoica. Clipper: A low-latency online prediction serving system. In 14th {USENIX} Symposium on Networked Systems Design and Implementation ({NSDI} 17), pages 613--627, 2017.

5. InferLine

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