Direct Telemetry Access-Reference-Cited by-同舟云学术

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Direct Telemetry Access

Published:2023-09 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the ACM SIGCOMM 2023 Conference
language:
Short-container-title:

Author:

Langlet Jonatan¹^ORCID,Ben Basat Ran²^ORCID,Oliaro Gabriele³^ORCID,Mitzenmacher Michael⁴^ORCID,Yu Minlan⁴^ORCID,Antichi Gianni⁵^ORCID

Affiliation:

1. Queen Mary University of London, London, United Kingdom

2. University College London, London, United Kingdom

3. Carnegie Mellon University, Pittsburgh, USA

4. Harvard University, Cambridge, United States of America

5. Politecnico di Milano, Milan, Italy

Funder

NSF (National Science Foundation)

EPSRC

European Union - NRRP

SRC & DARPA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3603269.3604827

Reference76 articles.

1. 2023. Direct Telemetry Access source code. https://github.com/jonlanglet/DTA. (2023). 2023. Direct Telemetry Access source code. https://github.com/jonlanglet/DTA. (2023).

2. CONGA

3. Remote Memory Calls

4. Michael P Andersen and David E Culler . 2016 . Btrdb: Optimizing storage system design for timeseries processing. In 14th {USENIX} Conference on File and Storage Technologies ({FAST} 16). 39--52. Michael P Andersen and David E Culler. 2016. Btrdb: Optimizing storage system design for timeseries processing. In 14th {USENIX} Conference on File and Storage Technologies ({FAST} 16). 39--52.

5. Arista. 2022. Telemetry and Analytics. https://www.arista.com/en/solutions/telemetry-analytics. (2022). Accessed: 2022-02-02. Arista. 2022. Telemetry and Analytics. https://www.arista.com/en/solutions/telemetry-analytics. (2022). Accessed: 2022-02-02.

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