Appdynamics, Inc.

Computer Associates Think, Inc.

Cisco Technology, Inc.

Ca, Inc.

Harness Inc.

Wily Technology, Inc.

Jyoti Bansal

Bhaskar Sunkara

David Isaiah Seidman

Mark Jacob Addleman

Vinay Srinivasaiah

Manoj Acharya

Binil David Thomas

Raghvendra Singh

Rishikesh Singh

Parnian Zargham

Sriram Parthasarathy

Awais Ahmed

Jeffrey Robert Cobb

Harish Nataraj

Ling Thio

Ajay Chandel

Adam Leftik

Kartik Shankaranarayanan

Stuart Todd Rader

Zahur Peracha

Rajpal Singh Sodhi

Aditya Pandit

Haroon Rashid Ahmed

Bhaskar Sankara

A system monitors a network or web application provided by one or more distributed applications and provides data for each and every method instance in an efficient low-cost manner. Agents may monitor the performance of the distributed application by the web services and report monitoring data as runtime data to the remote server, for example a controller. The controller may analyze the data to identify one or more performance issues or 'hot spot' methods based on current or past performance, functionality, content, or business relevancy. Instructions and/or configuration information may be transmitted by the controller to the agents that correspond to a particular business transaction portion associated with a hot spot. The portions are then monitored to collect data associated with the hot spot and the hot spot data is reported back to the controller.

Automatic capture of detailed analysis information based on remote server analysis

A system can monitor applications and analyze the metrics to determine if one or more of the applications are regressing or performing as expected. The metric analysis includes performing a first short term data analysis and, if data is not as expected, a second short term analysis based on machine learning-based pattern recognition machines. If the short-term analysis finds the metrics aren't as expected, a long-term analysis is performed. The long-term analysis can compare chunks of streaming metric data to cached metric blocks and historical data, and can include a concept drift analysis.

Continuous system service monitoring using real-time short-term and long-term analysis techniques

The present system provides continuous delivery and service regression detection in real time based on log data. The log data is clustered based on textual and contextual similarity and can serve as an indicator for the behavior of a service or application. The clusters can be augmented with the frequency distribution of its occurrences bucketed at a temporal level. Collectively, the textual and contextual similarity clusters serve as a strong signature (e.g., learned representation) of the current service date and a strong indicator for predicting future behavior. Machine learning techniques are used to generate a signature from log data to represent the current state and predict the future behavior of the service at any instant in time.

Service regression detection using real-time anomaly detection of log data

A system monitors applications, analyzes metrics, and provides a dashboard that communicates whether an application is performing as expected. The metric analysis includes performing one or more of a first short term data analysis, a second short term analysis based on machine learning-based pattern recognition machines, and a long-term analysis is performed. Transaction performance metrices are determined based on the monitored of the application. The transaction performance metrices are scored, scaled, and aggregated into a single scaled representation for the application. The scaled application value is then reported to a user through a dynamically updated dashboard. The dashboard displays graphical information representing the health of monitored transactions over time. The reported information can be expanded to additional layers of detail.

Reporting continuous system service status based on using real-time short-term and long-term analysis techniques

The present system uses delegates installed in remote environments to called and transmit, to a remote manager, time series metric data (or data from which metrics can be determined) in real-time. The numerical time series data is persisted, and a learned representation is generated from the data, for example by discretization. The learned representation is then clustered, the clusters are compared to new data, anomalies are determined, and deviation scores are calculated for the anomalies. The derivation scores are compared to thresholds, and results are reported through, for example, a user interface, dashboard, and/or other mechanism.

Service regression detection using real-time anomaly detection of application performance metrics

The present technology monitors a web application provided by one or more services. A service may be provided by applications. The monitoring system provides end-to-end business transaction visibility, identifies performance issues quickly and has dynamical scaling capability across monitored systems including cloud systems, virtual systems and physical infrastructures. In instances, a request may be received from a remote application. The request may be associated with a distributed transaction. Data associated with the request may be detected. A distributed transaction identifier may be generated for a distributed transaction based on the data associated with the request.

Naming of distributed business transactions

The present technology may determine an anomaly in a portion of a distributed business application. Data can automatically be captured and analyzed for the portion of the application associated with the anomaly. By automatically capturing data for just the portion associated with the anomaly, the present technology reduces the resource and time requirements associated with other code-based solutions for monitoring transactions. A method for performing a diagnostic session for a request may begin with initiating collection of diagnostic data associated with a request. An application thread on each of two or more servers may be sampled. The application threads may be associated with the same business transaction and the business transaction may be associated with the request. The diagnostic data may be stored.

Dynamic baseline determination for distributed business transaction

A system determines the performance of a network within the context of an application using that network. Network data is collected and correlated with an application that uses the network as well as a distributed transaction implemented by the application. The collected network data is culled, and the remaining data is rolled up into one or more metrics. The metrics, selected network data, and other data are reported in the context of the application that implements part of the distributed transaction. In this manner, specific network performance and architecture data is reported along with application context information.

Application centric network experience monitoring

Dynamic baseline determination for distributed transaction

Conducting a diagnostic session for monitored business transactions

A system monitors a network or web application provided by one or more distributed applications and provides data for each and every method instance in an efficient low-cost manner. The web application may be provided by one or more web services each implemented as a virtual machine or one or more applications implemented on a virtual machine. Agents may be installed on one or more servers at an application level, virtual machine level, or other level. The agent may identify one or more hot spot methods based on current or past performance, functionality, content, or business relevancy. Based on learning techniques, efficient monitoring, and resource management, the present system may capture data for and provide analysis information for outliers of a web application with very low overhead.

Automatic capture of detailed analysis information for web application outliers with very low overhead

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San Francisco, CA, United States of America

Jyoti Bansal