Associate Professor Shan Lu received the Jay Lepreau Best Paper Award at the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI) 2016 for a paper she co-authored, Early Detection of Configuration Errors to Reduce Failure Damage. Other authors included Tianyin Xu, Xinxin Jin, Peng Huang, Yuanyuan Zhou, Long Jin from the University of California, San Diego, and Shankar Pasupathy from NetApp.

This is the 4th Best Paper and Distinguished Paper awards Shan has received for her co-authored papers in the past 4 years.

View the paper in full here.

Abstract:
Early detection is the key to minimizing failure damage induced by configuration errors, especially those errors in configurations that control failure handling and fault tolerance. Since such configurations are not needed for initialization, many systems do not check their settings early (e.g., at startup time). Consequently, the errors become latent until their manifestations cause severe damage, such as breaking the failure handling. Such latent errors are likely to escape from sysadmins’ observation and testing, and be deployed to production at scale.

Our study shows that many of today’s mature, widely-used software systems are subject to latent configuration errors (referred to as LC errors) in their critically important configurations—those related to the system’s reliability, availability, and serviceability. One root cause is that many (14.0%–93.2%) of these configurations do not have any special code for checking the correctness of their settings at the system’s initialization time.

To help software systems detect LC errors early, we present a tool named PCHECK that analyzes the source code and automatically generates configuration checking code (called checkers). The checkers emulate the late execution that uses configuration values, and detect LC errors if the error manifestations are captured during the emulated execution. Our results show that PCHECK can help systems detect 75+% of real-world LC errors at the initialization phase, including 37 new LC errors that have not been exposed before. Compared with existing detection tools, it can detect 31% more LC errors.

shanlu_0.jpg

Related News

More UChicago CS stories from this research area.
UChicago CS News

Five UChicago CS students named to Siebel Scholars Class of 2024

Oct 02, 2023
UChicago CS News

UChicago Computer Scientists Bring in Generative Neural Networks to Stop Real-Time Video From Lagging

Jun 29, 2023
UChicago CS News

UChicago Team Wins The NIH Long COVID Computational Challenge

Jun 28, 2023
UChicago CS News

UChicago Assistant Professor Raul Castro Fernandez Receives 2023 ACM SIGMOD Test-of-Time Award

Jun 27, 2023
UChicago CS News

Computer Science Displays Catch Attention at MSI’s Annual Robot Block Party

Apr 07, 2023
UChicago CS News

Professor Heather Zheng Named ACM Fellow

Jan 18, 2023
Video

Ian Foster – Better Information Faster: Programming the Continuum

Jan 06, 2023
UChicago CS News

Q&A: Ian Foster on Receiving the 2023 IEEE Internet Award

Jan 06, 2023
UChicago CS News

Professor Fred Chong Named IEEE Fellow

Dec 09, 2022
UChicago CS News

Associate Professor Diana Franklin Named ACM Distinguished Member

Dec 07, 2022
UChicago CS News

UChicago’s Parsl Project Pivots to Sustainability and Community with New Grants

Nov 17, 2022
man browsing Netflix
UChicago CS News

Trending Now: How Netflix Chills Our Free Will

Nov 14, 2022
arrow-down-largearrow-left-largearrow-right-large-greyarrow-right-large-yellowarrow-right-largearrow-right-smallbutton-arrowclosedocumentfacebookfacet-arrow-down-whitefacet-arrow-downPage 1CheckedCheckedicon-apple-t5backgroundLayer 1icon-google-t5icon-office365-t5icon-outlook-t5backgroundLayer 1icon-outlookcom-t5backgroundLayer 1icon-yahoo-t5backgroundLayer 1internal-yellowinternalintranetlinkedinlinkoutpauseplaypresentationsearch-bluesearchshareslider-arrow-nextslider-arrow-prevtwittervideoyoutube