Vmware, Inc.

Jinyi Lu

Darren Brown

Paul Pedersen

Xing Wang

Junyuan Lin

Keshav Mathur

Peng Gao

Leah Nutman

Nicholas Kushmerick

Apolak Borthakur

Gopal Harikumar

Shafi Khan

Computational methods and systems to detect anomalous behaving resources and objects of a distributed computing system are described. Multiple streams of metric data representing usage of various resources of the distributed computing system are sent to a management system of the distributed computing system. The management system updates a performance model based on newly received metric values of the streams of metric data. The updated performance model is used to detect changes in one or more of the streams of metric data. The changes may be an indication of anomalous behavior at resources and objects associated with the streams of metric data. An anomaly listener is notified of anomalous behavior by the resource or object when a change in one or more of the streams of metric data is detected.

Streaming anomaly detection

Various examples are disclosed for transitioning usage forecasting in a computing environment. Usage of computing resources of a computing environment are forecasted using a first forecasting data model and usage measurements obtained from the computing resources. A use of the first forecasting data model in forecasting the usage is transitioned to a second forecasting data model without incurring downtime in the computing environment. After the transition, the usage of the computing resources of the computing environment is forecasted using the second forecasting data model and the usage measurements obtained from the computing resources. The second forecasting data model exponentially decays the usage measurements based on a respective time period at which the usage measurements were obtained.

Exponential decay real-time capacity planning

Computational methods and systems for detecting and troubleshooting anomalous behavior in distributed applications executing in a distributed computing system are described herein. Methods and systems discover nodes comprising the application. Anomaly detection monitors the metrics associated with the nodes for anomalous behavior in order to identify an approximate point in time when anomalous behavior begins to adversely impact performance of the application. Anomaly detection also monitors logs messages associated with the nodes to detect anomalous behavior recorded in the log messages. When anomalous behavior is detected in either the metrics and/or the log messages an alert identifying the anomalous behavior is generated. Troubleshooting guides an administrator and/or application owner to investigate the root cause of the anomalous behavior. Appropriate remedial measures may be determined based on the root cause and automatically or manually executed to correct the problem.

Methods and systems for troubleshooting applications using streaming anomaly detection

Automated methods and systems described herein are directed to identifying potential root causes of a problem in a data center. Methods and systems receipt an alert or other notification of a problem occurring in a data center and a time when the problem was noticed. A search window is created based on the time and a stream of log messages generated in the search window is converted into a time dependent metric. An anomaly detection technique is applied to the metric to determine a start time of a problem. Logging events and key phrases in the log messages are identified in the search window and presented as potential root causes of the problem. The potential root cause may then be used by system administrators and/or tenants to diagnose the problem and execute remedial measures to correct the problem.

Methods and systems for determining potential root causes of problems in a data center using log streams

Various examples are disclosed for forecasting resource usage and computing capacity utilizing an exponential decay. In some examples, a computing environment can obtain usage measurements from a data stream over a time interval, where the usage measurements describe utilization of computing resource. The computing environment can generate a weight function for individual ones of the usage measurements, where the weight function exponentially decays the usage measurements based on a respective time period at which the usage measurements were obtained. The computing environment can forecast a future capacity of the computing resources based on the usage measurements and the weight function assigned to the individual ones of the usage measurements. The computing environment can further upgrade a forecast engine to use the exponential decay without resetting the forecast engine or its memory.

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Palo Alto, CA, United States of America

Jinyi Lu