## Title:
Harnessing System Observability for Modeling and Detecting Gray Failures

## Abstract:

Real-world distributed systems suffer unavailability due to various
types of failure. But existing fault-tolerance mechanisms usually
assume a simple fail-stop model, which cannot effectively capture many
faults that occurred in large-scale production systems. In this talk,
I will discuss this problem of gray failure with real-world examples
to show its broad scope and consequences. Due to its inherently subtle
nature, gray failure is difficult to characterize and detect. We argue
that the key feature of gray failure is differential observability. I
will present our solution based on this insight -- Panorama, a system
that exploits the inherent observability in large systems to detect
complex failures by using static analysis to convert any component in
a system into an in-situ observer.

## Bio:

Dr. Ryan Huang is an assistant professor in the Department of Computer
Science at Johns Hopkins University. His research spans broadly in
computer systems, with intersections of programming languages and
software engineering. He enjoys solving challenging system problems in
real-world settings and seeking impact. His co-authored work won the
OSDI '16 best paper award and was nominated for best paper award at
MICRO '18. Dr. Huang obtained his PhD from UC San Diego and his BS
from Peking University.