Demo Data from SIGIR 2026 / reviewers in the wild / expert
Zhenting Zhu
dblp:359/9119
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0007-1076-2091ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Hardware reliability and fault tolerance · 67% Cloud and datacenter computing · 33% |
Topics — the 1 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Hardware reliability and fault tolerance › error detection
silent data corruption detection |
0.9 | 1 | 2025 | Orthrus: Efficient and Timely Detection of Silent User Data Corruption in the Cloud with Resource-Adaptive Computation Validation · SOSP 2025 |
Methods — techniques the papers use, named apart from their topics
error injection · 0.9compiler support · 0.9asynchronous computation validation · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Orthrus: Efficient and Timely Detection of Silent User Data Corruption in the Cloud with Resource-Adaptive Computation ValidationabstractEven with substantial endeavors to test and validate processors, computational errors may still arise post-installation. One particular category of CPU errors transpires discreetly, without crashing applications or triggering hardware warnings. These elusive errors pose a significant threat by undermining user data, and their detection is challenging. This paper introduces Orthrus, a solution for the timely detection of silent user data corruption caused by post-installation CPU errors. Orthrus safeguards user data in cloud applications by providing simple annotations and compiler support for users to identify data operators and validating these operators asynchronously across cores while maintaining a low overhead (2%–6%), making it practical for production deployment. Our evaluation, using carefully injected errors, demonstrates that Orthrus can detect 87% of data corruptions with just a single core dedicated to validation, increasing to 91% and 96% when two and four cores are used, respectively. Chenxiao Liu, Zhenting Zhu, Quanxi Li, Yanwen Xia, Yifan Qiao 0002, Xiangyun Deng, Youyou Lu, Tao Xie 0001, Huimin Cui, Zidong Du, Guoqing Harry Xu, Chenxi Wang 0005 |
SOSP | 2 |