RIBBON: cost-effective and qos-aware deep learning model inference using a diverse pool of cloud computing instances

RIBBON: cost-effective and qos-aware deep learning model inference using a diverse pool of cloud computing instances. Li, B., Roy, R. B., Patel, T., Gadepally, V., Gettings, K., & Tiwari, D. In de Supinski, B. R., Hall, M. W., & Gamblin, T., editors, International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2021, St. Louis, Missouri, USA, November 14-19, 2021, pages 24, 2021. ACM.

Paper doi bibtex 2 downloads

@inproceedings{DBLP:conf/sc/LiRPGGT21,
  author       = {Baolin Li and
                  Rohan Basu Roy and
                  Tirthak Patel and
                  Vijay Gadepally and
                  Karen Gettings and
                  Devesh Tiwari},
  editor       = {Bronis R. de Supinski and
                  Mary W. Hall and
                  Todd Gamblin},
  title        = {{RIBBON:} cost-effective and qos-aware deep learning model inference
                  using a diverse pool of cloud computing instances},
  booktitle    = {International Conference for High Performance Computing, Networking,
                  Storage and Analysis, {SC} 2021, St. Louis, Missouri, USA, November
                  14-19, 2021},
  pages        = {24},
  publisher    = {{ACM}},
  year         = {2021},
  url          = {https://doi.org/10.1145/3458817.3476168},
  doi          = {10.1145/3458817.3476168},
  timestamp    = {Mon, 05 Feb 2024 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/conf/sc/LiRPGGT21.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

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