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Cheap & Fast File-aaS for AI by Combining Scale-out Virtiofs, Block layouts and Delegations

Published: 16 September 2024 Publication History

Abstract

File service Supply & Demand forces are changing: on the demand side, AI has significantly increased single-tenant File-aaS performance requirements. On the supply side, economic forces are moving the clients from traditional servers to rack-scale computers. Rack-scale computers encourage heterogeneous compute and are equipped with DPUs (Data-Processing Unit, also known as Smart NIC). DPUs offer higher efficiency, but may be over utilized at times. For this reason we would like our software to be flexible and run on general purpose compute when DPU utilization is high.

References

[1]
Stephen Fridella, David L. Black, and Jason Glasgow. Parallel NFS (pNFS) Block/Volume Layout. RFC 5663, January 2010.
[2]
Peter Jan Gootzen, Jonas Pfefferle, Radu Stoica, and Animesh Trivedi. DPFS: DPU-powered file system virtualization. In SYSTOR '23: Proc. of the 16th ACM Int. Conf. on Systems and Storage, June 2023.

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Published In

cover image ACM Conferences
SYSTOR '24: Proceedings of the 17th ACM International Systems and Storage Conference
September 2024
212 pages
ISBN:9798400711817
DOI:10.1145/3688351
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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  • Technion: Israel Institute of Technology

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 September 2024

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Author Tags

  1. Cloud Storage
  2. DPU
  3. Distributed Systems
  4. File aaS
  5. NFS
  6. Virtiofs

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  • Poster
  • Research
  • Refereed limited

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SYSTOR '24
Sponsor:

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SYSTOR '24 Paper Acceptance Rate 14 of 38 submissions, 37%;
Overall Acceptance Rate 108 of 323 submissions, 33%

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