Splunk Inc.

Cisco Technology, Inc.

Subramaniam Baskaran

Omprakaash Thoppai

Tristan Antonio Fletcher

Michael Margulis

Marc Andre Chene

Sakib Mehasanewala

Joel Schoenberg

Syam Bollu

Esha Desai

Rehan Mulla

Vinay Sheshadri

Bixia Yan

Ian Edward Torbett

Satya Venkata Bharani Suresh Vasabhaktula

Anudeep Rentala

An example method of implementing a layered feature set management model by a service monitoring system includes: monitoring a feature set configuration associated with a specified application instance; setting, based on the feature set configuration, a feature set level transition marker associated with the specified application instance; identifying, based on a current feature set level associated with the specified application instance and the feature set level transition marker, a new feature set level associated with the specified application instance; identifying a new feature set corresponding to the new feature set level and one or more roles associated with a specified user; and configuring a graphical user interface (GUI) enabling the new feature set for the specified user of the specified application instance.

Layered feature set levels in service monitoring system

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 computer implemented method includes establishing, by a data intake and query system, a network connection between the data intake and query system and an application and infrastructure monitoring platform. The data intake and query system receives a data stream from the application and infrastructure monitoring platform. The computer implemented method further includes transforming the data stream while receiving the data stream to obtain a transformed data stream. Further, the transformed data stream is analyzed while receiving the data stream to generate analysis results, which are presented.

Monitoring platform job integration in computer analytics system

An example method includes collecting, at a computing system of a data intake and query system, source data corresponding to an instance of a service hosted by a service provider, wherein the service provider hosts the service on a network of the service provider, identifying in the source data a set of metrics for the instance of the service, applying a machine learning model to the set of metrics to determine a classification for the set of metrics, generating, using the classification, a recommendation, wherein the recommendation relates to usage by the instance of the service of one or more physical resources of the service provider, and transmitting, for receipt by a client device, data comprising the recommendation, wherein the data enables display on the client device of a visualization comprising the recommendation.

Service provider instance recommendations using machine-learned classifications and reconciliation

Information technology networked cloud service monitoring

Integration in computer analytics system

Growing community of inventors

Foster City, CA, United States of America

Subramaniam Baskaran