Abstract: Herein are machine learning techniques that adjust reconstruction loss of a reconstructive model, such as a principal component analysis (PCA), based on importances of features. In an embodiment having a reconstructive model that more or less accurately reconstructs its input, a computer measures, for each feature, a respective importance that is based on the reconstructive model. For example, importance may be based on grading samples that the reconstructive model correctly or incorrectly inferenced. For each feature during production inferencing, a respective original loss from the reconstructive model measures a difference between a value of the feature in an input and a reconstructed value of the feature generated by the reconstructive model. For each feature, the respective importance of the feature is applied to the respective original loss to generate a respective weighted loss, which compensates for concept drift.

Abstract: Techniques are described herein for accurately measuring the reliability of storage systems. Rather than relying on a series of approximations, which may produce highly optimistic estimates, the techniques described herein use a failure distribution derived from a disk failure data set to derive reliability metrics such as mean time to data loss (MTTDL) and annual durability. A new framework for modeling storage system dynamics is described herein. The framework facilitates theoretical analysis of the reliability. The model described herein captures the complex structure of storage systems considering their configuration, dynamics, and operation. Given this model, a simulation-free analytical solution to the commonly used reliability metrics is derived. The model may also be used to analyze the long-term reliability behavior of storage systems.

Abstract: Techniques are described herein for accurately measuring the reliability of storage systems. Rather than relying on a series of approximations, which may produce highly optimistic estimates, the techniques described herein use a failure distribution derived from a disk failure data set to derive reliability metrics such as mean time to data loss (MTTDL) and annual durability. A new framework for modeling storage system dynamics is described herein. The framework facilitates theoretical analysis of the reliability. The model described herein captures the complex structure of storage systems considering their configuration, dynamics, and operation. Given this model, a simulation-free analytical solution to the commonly used reliability metrics is derived. The model may also be used to analyze the long-term reliability behavior of storage systems.