No one has mentioned "gray failure", https://www.cs.jhu.edu/~huang/paper/grayfailure-hotos17.pdf https://www.cs.jhu.edu/~huang/paper/grayfailure-hotos17.pdf >

No one has mentioned "gray failure", https://www.cs.jhu.edu/~huang/paper/grayfailure-hotos17.pdf https://www.cs.jhu.edu/~huang/paper/grayfailure-hotos17.pdf > Cloud scale provides the vast resources necessary to replace failed components, but this is useful only if those failures can be detected. For this reason, the major availability breakdowns and performance anomalies we see in cloud environments tend to be caused by subtle underlying faults, i.e., gray failure rather than fail-stop failure. In this paper, we discuss our experiences with gray failure in production cloud-scale systems to show its broad scope and consequences. We also argue that a key feature of gray failure is differential observabil- ity: that the system’s failure detectors may not notice problems even when applications are afflicted by them. This realization leads us to believe that, to best deal with them, we should focus on bridging the gap between different components’ perceptions of what constitutes failure.