Splunk Inc.

Marc Andre Chene

Tristan Antonio Fletcher

Arun Ramani

Anupadmaja Raghavan

Omprakaash Thoppai

Subramaniam Baskaran

Sakib Mehasanewala

Steve Yu Zhang

Christopher Madden Pride

Ledion Bitincka

Vishal Patel

Alexander Douglas James

David Marquardt

David Everett Simmen

Igor Stojanovski

Thomas Allan Haggie

Clint Sharp

Hailun Yan

Murugan Kandaswamy

Amrittpal Singh Bath

Pratiksha Shah

Esguerra Ma Kharisma

Systems and methods ingest machine data including logs, metadata, and cost and usage information from multiple heterogeneous cloud services. The machine data is saved as events. An application retrieves the metadata, events, metrics, and logs and causes an easy to understand visual representation of costs, resource usage, and non-compliance for each of a client's cloud services. Further, the data across the client's multiple heterogeneous cloud services is normalized to provide visual representations that compare the costs, resource usage, and non-compliance across the client's multiple heterogeneous cloud services. Further, machine learning aspects of the application can provide recommendations and trend analysis for cloud service asset usage.

Networked cloud service monitoring

A service monitoring system (SMS) produces key performance indicator (KPI) scores that indicate the performance of a service. To produce the KPI scores, the SMS may process the data for a large number of machine entities that perform the service. This data can be processed on a per-entity basis to produce a per-entity KPI score representing the contribution of a particular machine to the overall KPI. The per-entity KPI scores can be transformed to statistical representations which can be visualized as a distribution stream graph. The visualization may be presented with interactive aspects. Automatic entity definitions may also be generated based on content derived from the processed data.

Automatic entity definitions based on derived content

Information technology networked cloud service monitoring

The disclosed embodiments include a method performed by a data intake and query system. The method includes ingesting each metric including at least one key value and a measured value taken of a computing resource, and storing each metric in an index of a metrics store, where the index defines at least one dimension populated with the at least one key value and a measure populated with the measured value. The method further includes cataloging metadata in a metrics catalog, where the metadata is related to the metrics stored in the metrics store, performing an analysis of metrics data included in the metrics store and/or the metrics catalog to obtain results, and causing display of the results or an indication of the results on a display device.

Metrics store system

Methods are disclosed to take advantage of the early collection of machine data from a new or changed entity in a computing environment in order to update the definitional information about entities used by a service monitoring system. In some embodiments, the process undertaken to recognize new or changed entities in an IT environment from collected machine data may be informed by the expertise of a particular subject matter area by installing that intelligence in a codified form packaged as a domain add-on to the service monitoring system.

Automatic entity definitions

Growing community of inventors

Bellevue, WA, United States of America

Marc Andre Chene