Abstract: Computer-implemented methods and systems described herein perform intelligent sampling of application traces generated by an application. Computer-implemented methods and systems determine different sampling rates based on frequency of occurrence of trace types and/or frequency of occurrence of durations of the traces. Each sampling rate corresponds to a different trace type and/or different duration. The sampling rates for low frequency trace types and durations are larger than the sampling rates for high frequency trace types and durations. The relatively larger sampling rates for low frequency trace types and low frequency durations ensures that low frequency trace types and low frequency durations are sampled in sufficient numbers and are not passed over during sampling of the application traces. The set of sampled traces are stored in a data storage device.

Abstract: Automated methods and systems for identifying and resolving performance problems of objects of a data center are described. The automated methods and systems construct a model for identifying objects of the datacenter that are experiencing performance problems based on baseline distributions of events of the objects in a historical time period and event distributions of events of the objects in a time window located outside the historical time period. A root causes and recommendations database is constructed for resolving performance problems based on remedial measures previously performed for resolving performance problems. The model is used to monitor the objects of data center for runtime performance problems. When a performance problem with an object is detected, the root causes and recommendations database is used to identify a root cause of the performance problem and generate a recommendation for resolving the performance problem in near real time.

Abstract: The current document is directed to methods and systems that employ call traces collected by one or more call-trace services to generate call-trace-classification rules to facilitate root-cause analysis of distributed-application operational problems and failures. In a described implementation, a set of automatically labeled call traces is partitioned by the generated call-trace-classification rules. Call-trace-classification-rule generation is constrained to produce relatively simple rules with greater-than-threshold confidences and coverages. The call-trace-classification rules may point to particular services and service failures, which provides useful information to distributed-application and distributed-computer-system managers and administrators attempting to diagnose operational problems and failures that arise during execution of distributed applications within distributed computer systems.

Abstract: The current document is directed to methods and systems that employ call traces collected by one or more call-trace services to generate call-trace-classification rules to facilitate root-cause analysis of distributed-application operational problems and failures. In a described implementation, a set of automatically labeled call traces is partitioned by the generated call-trace-classification rules. Call-trace-classification-rule generation is constrained to produce relatively simple rules with greater-than-threshold confidences and coverages. The call-trace-classification rules may point to particular services and service failures, which provides useful information to distributed-application and distributed-computer-system managers and administrators attempting to diagnose operational problems and failures that arise during execution of distributed applications within distributed computer systems.

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: Automated methods and systems for resolving potential root causes of performance problems with applications executing in a data center are described. The automated methods use machine learning to train an inference model that relates event types recorded in metrics, log messages, and traces of an application to values of a key performance indicator (“KPI”) of the application. The methods use the trained inference model to determine which of the event types are important event types that relate to performance of the application. In response to detecting a run-time performance problem in the KPI, the methods determine which of the important event has a higher probability of being the potential root cause of the performance problem. A graphical user interface displays an alert that identifies the application as having the run-time performance problem, identity of the important event types, and at least one recommendation for remedying the performance problem.

Abstract: Automated methods and systems for identifying problems associated with objects of a data center are described. Automated methods and systems are performed by an operations management server. For each object, the server determines a baseline distribution from historical events that are associated with a normal operational state of an object. The server determines a runtime distribution of runtime events that are associated with the object and detected in a runtime window of the object. The management server monitors runtime performance of the object while the object is running in the datacenter. When a performance problem is detected, the management server determines a root cause of a performance problem based on the baseline distribution and the runtime distribution and displays an alert in a graphical user interface of a display.

Abstract: Automated methods and systems for identifying and resolving performance problems of objects of a data center are described. The automated methods and systems construct a model for identifying objects of the datacenter that are experiencing performance problems based on baseline distributions of events of the objects in a historical time period and event distributions of events of the objects in a time window located outside the historical time period. A root causes and recommendations database is constructed for resolving performance problems based on remedial measures previously performed for resolving performance problems. The model is used to monitor the objects of data center for runtime performance problems. When a performance problem with an object is detected, the root causes and recommendations database is used to identify a root cause of the performance problem and generate a recommendation for resolving the performance problem in near real time.

Abstract: Automated methods and systems for identifying problems associated with objects of a data center are described. Automated methods and systems are performed by an operations management server. For each object, the server determines a baseline distribution from historical events that are associated with a normal operational state of an object. The server determines a runtime distribution of runtime events that are associated with the object and detected in a runtime window of the object. The management server monitors runtime performance of the object while the object is running in the datacenter. When a performance problem is detected, the management server determines a root cause of a performance problem based on the baseline distribution and the runtime distribution and displays an alert in a graphical user interface of a display.

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: Automated methods and systems for compressing log messages stored in a log message databased are described herein. The automated methods and systems perform lossy compression of an original set of log messages by identifying log messages that represent each of the various types of events recorded in the original set. The log messages in the original set are overwritten by corresponding representative log messages. Source coding is used to construct a source coding scheme and variable length binary codewords for each of the representative log messages. The representative log messages are replaced by the codewords, which occupies significantly less storage space than the original set. The lossy compressed set of log messages can be decompressed to obtain the representative log messages using the source coding scheme.

Abstract: Automated computer-implemented processes and systems manage and troubleshoot a service provided by a distributed application executing in a distributed computing system. Processes query objects of the distributed computing system to identify candidate objects for addition to the service. Processes generate recommendations in a graphical user interface (“GUI”) that enable a user to select and enroll the one or more candidate objects into the service via the GUI. Processes monitor a key performance indicator (“KPI”) of the service for violations of a corresponding service level object (“SLO”) threshold. When the KPI violates the SLO threshold, processes determine a root cause of a performance problem with the service based on a metric-association rule associated with the KPI violation of the SLO threshold and displays the performance problem and a recommendation that corrects the performance problem in a GUI.

Abstract: Methods and systems are directed to discovering problem incidents in a distributed computing system. Events corresponding to historical problems incidents for the distributed computing system are retrieved from a data base. Sets of representative events of the various historical problem incidents for the distributed computing system are determined. A runtime problem incident in the distributed computing system is characterized by runtime events. The runtime problem incident is classified as corresponding to a historical problem incident of the historical problem incidents based on the runtime events and the sets of representative events. Remedial measures used to correct the historical problem incident may be used to correct the runtime problem.

Abstract: Automated processes and systems for detecting abnormally behaving objects of a distributed computing system are described. Processes and systems obtain metrics that are generated in a historical time window and are associated with an object of the distributed computing system. Processes and system use the metrics to compute a time-dependent system indicator over the historical time window. Each value of the system indicator corresponds to a point in time of the historical time window when the object was in a normal or an abnormal state. Processes and systems use the normal and abnormal states of the system indicator in the historical time window to train a state classifier that is used to detect run-time abnormal behavior of the object. When the state classifier identifies abnormal behavior of the object, an alert is generated, indicating the abnormal behavior of the object.

Abstract: Computer-implemented methods and systems described herein perform intelligent sampling of application traces generated by an application. Computer-implemented methods and systems determine different sampling rates based on frequency of occurrence of normal traces and erroneous traces of the application. The sampling rates for low frequency normal and erroneous traces are larger than the sampling rates for high frequency normal and erroneous traces. The relatively larger sampling rates for low frequency trace ensures that low frequency traces are sampled in sufficient numbers and are not passed over during sampling of the application traces. The sampled normal and erroneous traces are stored in a data storage device.

Abstract: Computer-implemented methods and systems described herein perform intelligent sampling of application traces generated by an application. Computer-implemented methods and systems determine different sampling rates based on frequency of occurrence of trace types and/or frequency of occurrence of durations of the traces. Each sampling rate corresponds to a different trace type and/or different duration. The sampling rates for low frequency trace types and durations are larger than the sampling rates for high frequency trace types and durations. The relatively larger sampling rates for low frequency trace types and low frequency durations ensures that low frequency trace types and low frequency durations are sampled in sufficient numbers and are not passed over during sampling of the application traces. The set of sampled traces are stored in a data storage device.

Abstract: The current document is directed to methods and systems that employ distributed-computer-system metrics collected by one or more distributed-computer-system metrics-collection services, call traces collected by one or more call-trace services, and attribute values for distributed-computer-system components to identify attribute dimensions related to anomalous behavior of distributed-computer-system components. In a described implementation, nodes correspond to particular types of system components and node instances are individual components of the component type corresponding to a node. Node instances are associated with attribute values and node are associated with attribute-value spaces defined by attribute dimensions. A set of call traces is partitioned, by clustering.

Abstract: Automated computational methods and systems to classify and troubleshoot problems in information technology (“IT”) systems or services provided by a distributed computing system are described. Each IT system of the distribution computing system or IT service provided by the distributed computing system has an associated key performance indicator (“KPI”) used to monitor performance of the IT system or service. When real-time KPI data violates a KPI threshold, a real-time event-type distribution is computed from event messages generated by event sources associated with the IT system or service following the threshold violation. The real-time event-type distribution is compared with historical event-type distributions recorded for the KPI data in order to identify the problem and execute remedial action to resolve the problem.

Abstract: The current document is directed to methods and systems that employ call traces collected by one or more call-trace services to generate call-trace-classification rules to facilitate root-cause analysis of distributed-application operational problems and failures. In a described implementation, a set of automatically labeled call traces is partitioned by the generated call-trace-classification rules. Call-trace-classification-rule generation is constrained to produce relatively simple rules with greater-than-threshold confidences and coverages. The call-trace-classification rules may point to particular services and service failures, which provides useful information to distributed-application and distributed-computer-system managers and administrators attempting to diagnose operational problems and failures that arise during execution of distributed applications within distributed computer systems.

Abstract: The current document is directed to methods and systems that employ call traces collected by one or more call-trace services to generate call-trace-classification rules to facilitate root-cause analysis of distributed-application operational problems and failures. In a described implementation, a set of automatically labeled call traces is partitioned by the generated call-trace-classification rules. Call-trace-classification-rule generation is constrained to produce relatively simple rules with greater-than-threshold confidences and coverages. The call-trace-classification rules may point to particular services and service failures, which provides useful information to distributed-application and distributed-computer-system managers and administrators attempting to diagnose operational problems and failures that arise during execution of distributed applications within distributed computer systems.