Vmware, Inc.

Naira Movses Grigoryan

Ashot Nshan Harutyunyan

Arnak Poghosyan

Mazda A Marvasti

Nicholas Kushmerick

Clement Pang

Yanislav Yankov

George Oganesyan

Vahe Khachikyan

Vardan Movsisyan

Dev A Nag

Vahan Tadevosyan

Astghik Grigoryan

Davit Baghdasaryan

Harutyun Beybutyan

Tigran Bunarjyan

Nina Karapetyan

Vardan Hovhannisyan

Khachatur Nazaryan

Ruzan Hovhannisyan

Meruzhan Kerobyan

Avetik Hovhannisyan

Vaghinak Saghatelyan

Amak Poghosyan

Artur Grigoryan

Karen Aghajanyan

Tigran Avagimyants

Karen Avagyan

Hovhannes Antonyan

Nshan Sharoyan

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.

Methods and systems for discovering incidents through clustering of alert occurring in a data center

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.

Methods and systems for intelligent sampling of application traces

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.

Methods and systems for identifying and resolving root causes of performance problems in data center object

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. Call-trace-classification rules that are useful in multiple diagnoses are maintained as diagnosis tools for future diagnoses.

Automated methods and systems that facilitate root cause analysis of distributed-application operational problems and failures

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. A first dataset is collected during normal distributed-application operation and a second dataset is collected during problem-associated or failure-associated operation of the distributed application. The first and second datasets are used to generate noise-subtracted call-trace-classification rules and/or diagnostic suggestions.

Automated methods and systems that facilitate root-cause analysis of distributed-application operational problems and failures by generating noise-subtracted call-trace-classification rules

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.

Automated methods and systems for identifying problems in data center objects

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.

Processes and systems that detect abnormal behavior of objects of a distributed computing system

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. Using attribute values and call traces, attribute dimensions that are likely related to particular anomalous behaviors of distributed-computer-system components are determined by decision-tree-related analyses for each partition and are reported to one or more computational entities to facilitate resolution of the anomalous behaviors.

Methods and systems that identify dimensions related to anomalies in system components of distributed computer systems using clustered traces, metrics, and component-associated attribute values

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.

Automated methods and systems to classify and troubleshoot problems in information technology systems and services

Methods and systems described herein are directed to troubleshooting anomalous behavior in a data center. Anomalous behavior in an object of a data center, such as a computational resource, an application, or a virtual machine ('VM'), may be related to the behavior of other objects at different hierarchies of the data center. Methods and systems provide a graphical user interface that enables a user to select a selected metric associated with an object of the data center experiencing a performance problem. Unexpected metrics of an object topology of the data center that correspond to the performance problem are identified. A recommendation for executing remedial measures to correct the performance problem is generated based on the unexpected metrics.

Methods and systems for troubleshooting anomalous behavior in a data center

Automated methods and systems to determine a baseline event-type distribution of an event source and use the baseline event type distribution to detect changes in the behavior of the event source are described. In one implementation, blocks of event messages generated by the event source are collected and an event-type distribution is computed for each of block of event messages. Candidate baseline event-type distributions are determined from the event-type distributions. The candidate baseline event-type distribution has the largest entropy of the event-type distributions. A normal discrepancy radius of the event-type distributions is computed from the baseline event-type distribution and the event-type distributions. A block of run-time event messages generated by the event source is collected. A run-time event-type distribution is computed from the block of run-time event messages. When the run-time event-type distribution is outside the normal discrepancy radius, an alert is generated indicating abnormal behavior of the event source.

Methods and systems to determine baseline event-type distributions of event sources and detect changes in behavior of event sources

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. Using attribute values and call traces, attribute dimensions that are likely related to particular anomalous behaviors of distributed-computer-system components are determined by decision-tree-related analyses and are reported to one or more computational entities to facilitate resolution of the anomalous behaviors.

Methods and systems that identify dimensions related to anomalies in system components of distributed computer systems using traces, metrics, and component-associated attribute values

Computational processes and systems are directed to detecting abnormally behaving objects of a distributed computing system. An object can be a physical or a virtual object, such as a server computer, application, VM, virtual network device, or container. Processes and systems identify a set of metrics associated with an object and compute an indicator metric from the set of metrics. The indicator metric is used to label time stamps that correspond to outlier metric values of the set of metrics. The metrics and outlier time stamps are used to compute rules by machine learning. Each rule corresponds to a subset or combination of metrics and represents specific threshold conditions for metric values. The rules are applied to run-time metric data of the metrics to detect run-time abnormal behavior of the object.

Processes and systems that detect object abnormalities in a distributed computing system

Methods and systems that estimate a degree of abnormality of a complex system based on historical time-series data representative of the complex system's past behavior and using the historical degree of abnormality to determine whether or not a degree of abnormality determined from current time-series data representative of the same complex system's current behavior is worthy of attention. The time-series data may be metric data that represents behavior of a complex system as a result of successive measurements of the complex system made over time or in a time interval. A degree of abnormality represents the amount by which the time-series data violates a threshold. The larger the degree of abnormality of the current time-series data is from the historical degree of abnormality, the larger the violation of the thresholds and the greater the probability the violation in the current time-series data is worthy of attention.

Methods and systems that estimate a degree of abnormality of a complex system

The current document is directed to methods and subsystems within computing systems, including distributed computing systems, that collect, store, process, and analyze population metrics for types and classes of system components, including components of distributed applications executing within containers, virtual machines, and other execution environments. In a described implementation, a graph-like representation of the configuration and state of a computer system included aggregation nodes that collect metric data for a set of multiple object nodes and that collect metric data that represents the members of the set over a monitoring time interval. Population metrics are monitored, in certain implementations, to detect outlier members of an aggregation.

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Yerevan, Armenia

Naira Movses Grigoryan