Patents

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.

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An unsupervised pattern extraction system and method for extracting user interested patterns from various kinds of data such as system-level metric values, system call traces, and semi-structured or free form text log data and performing holistic root cause analysis for distributed systems. The distributed sys­tem includes a plurality of computer machines or smart devices. The system consists of both real time data collection and analytics functions. The analytics functions automatically extract event patterns and recognize recurrent events in real time by analyzing collected data streams from different sources. A root cause analysis component analyzes the extracted events and identifies both correlation and causality relationships among different components to pinpoint root cause of a system anomaly. Furthermore, an anomaly impact prediction component estimates the impact scope of the detected anomaly and raises early alarms about impending service outages or application performance degradations based on our identified correlation and causality relationships.

An unsupervised pattern extraction system and method for extracting incident and root cause patterns from various kinds of machine data such as system-level metric values, system call traces, and semi-structured or free form text log data and performing holistic root cause analysis for distributed systems. The system utilizing Natural Language Processing and machine learning techniques to extract incident and root cause information from received incident reports and other system data. The system consists of both real time data collection (104) and analytics functions (200). The previously reported incident data is used to discover and apply remediation techniques to utilize prior remediation efforts to automatically classify and correct incidents. The system may then annotate a remediation data file with the technique applied. The system will utilize prior known remediation techniques for identified categories to predict and prevent future issues.