Karthik Pattabiraman is a Professor of Electrical and Computer Engineering at the University of British Columbia (UBC). He received his MS and PhD in computer science from the University of Illinois at Urbana Champaign (UIUC) in 2004 and 2009, and spent a postdoctoral year at Microsoft Research (MSR), Redmond before joining UBC in 2010. His research interests are in dependability, security, and software engineering. Karthik has won multiple awards such as the Inaugural Rising Star in Dependability Award, 2020, from the IEEE and the IFIP, the distinguished early-career alumnus award from the University of Illinois (UIUC), CS department, 2018, and multiple UBC-wide awards for excellence in research and mentoring. Together with his students and collaborators, he has published over 100 papers, many of which have received distinguished paper awards at venues such as DSN and ICSE. He is a distinguished contributor of the IEEE computer society, a distinguished member of the ACM, and the vice-chair of the IFIP Working Group on dependable computing and fault-tolerance (WG 10.4). A detailed biography may found at: https://blogs.ubc.ca/karthik/about/full-bio/
Fault Injection is Dead. Long Live Fault Injection!
Fault injection (FI), or fault simulation, is one of the most popular methods to evaluate the dependability of real-world systems. FI has a rich history in our community, going back nearly five decades. FI has led to fundamental insights into the dependability of numerous real-world systems, and has spurred significant research in diverse areas. There have also been many mature fault injection tools developed, many of which have been successfully deployed in industry. Yet, there is a perception in other communities that FI is a solved research problem, and that the only remaining challenges in FI are engineering issues. In this talk, I will argue that this is far from the truth, and that there are still many unaddressed research questions pertaining to the accurate emulation of hardware and software faults. I will also argue that FI is even more important in new domains such as machine learning (ML) and autonomous systems. I will draw upon my research group’s work in the FI area over the last decade or so, as well as discussions with different stakeholders, to outline what I believe are the grand challenges for FI.
This is joint work with my students and colleagues at UBC, and industry collaborators.