Abstract: Automated, computer-implemented methods and systems for resolving performance problems with objects executing in a data center are described. The automated methods use machine learning to train a model that comprises rules defining relationships between probabilities of event types of in log messages and values of a key performance indictor (“KPI”) of the object over a historical time period. When a KPI violates a corresponding threshold, the rules are used to evaluate run time log messages that describe the probable root cause of the performance problem. An alert identifying the KPI threshold violation, and the log messages are displayed in a graphical user interface of an electronic display device.

Abstract: Automated, computer-implemented methods and systems describe herein resolve performance problems with objects executing in a data center. The operations manager uses machine learning to train an inference model that relates probability distributions of event types of log messages of the object to a key performance indicator (“KPI”) of the object. The operations manager monitors the KPI for run-time KPI values that violates a KPI threshold. When the KPI violates the threshold, the operations manager determines probabilities of event types of log messages recorded in a run-time interval and uses the inference model to determine event types of the probabilities of event types of log messages in the run-time interval to determine a root cause of the performance problem. The inference models can be used to identify log messages of event types that correspond to potential performance problems with data center objects and execute appropriate remedial measures to avoid the problems.

Abstract: Automated computer-implemented methods and systems for resolving performance problems with objects executing in a data center are described. The automated methods use machine learning to obtain rules defining relationships between probabilities of event types of in log messages and performance problems identified by a key performance indictor (“KPI”) of the object. When a KPI violates a corresponding threshold, the rules are used to evaluate run time log messages that describe the probable root cause of the performance problem. An alert identifying the KPI threshold violation, and the log messages are displayed in a graphical user interface of an electronic display device.

Abstract: Automated computer-implemented methods and systems for discovering clusters of alerts triggered by abnormal events occurring with objects in a data center are described. In one aspect, alerts with start times in a sliding run-time window are retrieved from an alerts database. Each alert corresponds to a run-time event occurring with an object of the data center. Clusters of alerts in the sliding run-time window are detected based on the start times of the alerts and topological proximity of the objects. High priority alerts in the clusters of alerts are determined based on alert types. The events associated with discovered clusters of alerts and high priority alerts are displayed in a graphical user interface (“GUI”). Time evolution clustering of alerts and coverage evolution of alerts are over time based on the start times of the alerts and topological proximity of objects exhibiting abnormal behavior in the data center.

Abstract: The current document is directed to improved methods and systems that collect, generate, and store multidimensional metric data used for monitoring, management, and administration of computer systems and that continuously optimize sampling rates for metric data. Multiple different metric-data streams are sampled for each of multiple different distributed-computer-system objects, and are hierarchically organized into a number of different individual and multidimensional metric-data streams. The sampling rates for the different individual and multidimensional metric-data streams are correspondingly hierarchically optimized in order to avoid oversampling the metric data while preserving the relevant information content of the sampled metric data for downstream data analysis.

Abstract: The current document is directed to methods and systems that collect metric data within computing facilities, including large data centers and cloud-computing facilities. In a described implementation, two or more metric-data sets are combined to generate a multidimensional metric-data set. The multidimensional metric-data set is compressed for efficient storage by clustering the multidimensional data points within the multidimensional metric-data set to produce a covering subset of multidimensional data points and by then representing the multidimensional-data-point members of each cluster by a cluster identifier rather than by a set of floating-point values, integer values, or other types of data representations. The covering set is constructed to ensure that the compression does not result in greater than a specified level of distortion of the original data.

Abstract: The current document is directed to methods and systems that automatically generate training data for machine-learning-based components used by a metric-data processing-and-analysis component of a distributed computer system, a subsystem within a distributed computer system, or a standalone metric-data processing-and-analysis system. The training data sets are labeled using categorical KPI values. The machine-learning-based components are applied to metric data both for predicting anomalous operational behaviors and problems within the distributed computer system and for determination of potential causes of anomalous operational behaviors and problems within the distributed computer system. Training of machine-learning-based components is carried out concurrently and asynchronously with respect to other metric-data collection, aggregation, processing, storage, and analysis tasks.

Abstract: An AI-driven support system is described herein. This system includes a request formed from least one of a support request and a knowledge base. The system also includes an extractor module made up of a data pipeline configured to construct a training dataset from an input of at least one of said support request and said knowledge base, a training pipeline configured to take said training dataset use a BERT language model to generate at least one feature vector, and an evaluation pipeline fit to compare outputs from at least one iteration of said training pipeline, as well as output at least one parsed feature vector.