Saurabh Kadekodi

Saurabh Kadekodi

Saurabh Kadekodi is a research scientist working in the Storage Analytics team. He specializes in reliability anf performance of large-scale storage clusters. Saurabh completed his Ph.D. from Carnegie Mellon University as part of the Parallel Data Laboratory. Prior to that he has done his Masters from Northwestern University and bachelors from Pune Institute of Computer Technology, India.

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Thesios: Synthesizing Accurate Counterfactual I/O Traces from I/O Samples
Mangpo Phothilimthana
Saurabh Kadekodi
Soroush Ghodrati
Selene Moon
Martin Maas
ASPLOS 2024, Association for Computing Machinery
Practical Design Considerations for Wide Locally Recoverable Codes (LRCs)
Saurabh Kadekodi
Shashwat Silas
Dave Clausen
Arif Merchant
File and Storage Technologies (FAST), USENIX (2023)
Tiger: disk-adaptive redundancy without placement restrictions
Saurabh Kadekodi
Francisco Maturana
Sanjith Athlur
Arif Merchant
Rashmi KV
Gregory R. Ganger
Tiger: disk-adaptive redundancy without placement restrictions (2022)
A Case for Packing and Indexing in Cloud File Systems
Saurabh Kadekodi
Bin Fan
Adit Madan
Garth Gibson
Gregory R. Ganger
USENIX (2018)
Taking Linux Filesystems to the Space Age: Space Maps in Ext4
Saurabh Kadekodi
Shweta Jain
(2010)
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