Unsupervised behavior learning system and method for predicting performance anomalies in distributed computing infrastructures
US10311356B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 8, 2014 |
| Grant date | Jun 4, 2019 |
| Priority date | — |
| Expiry date | Feb 4, 2037 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/082
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
An unsupervised behavior learning system and method for predicting anomalies in a distributed computing infrastructure. The distributed computing infrastructure includes a plurality of computer machines. The system includes a first computer machine and a second computer machine. The second computer machine is configured to generate a model of normal and anomalous behavior of the first computer machine, where the model is based on unlabeled training data. The second computer machine is also configured to acquire real-time data of system level metrics of the first machine; determine whether the real-time data is normal or anomalous based on a comparison of the real-time data to the model; and predict a future failure of the first computer machine based on multiple consecutive comparisons of the real-time data to the model. Upon predicting a future failure of the first computer machine, generate a ranked set of system-level metrics which are contributors to the predicted failure of the first computer machine, and generate an alarm that includes the ranked set of system-level metrics. The model of normal and anomalous behavior may include a self-organizing map.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.