Splunk Inc.

Servicenow, Inc.

Ayyappa Muthusami

Ian Edward Torbett

Jeffrey Wen-Young Shih

Ankur Ashok Kath

Peter Wu

Tristan Antonio Fletcher

Susanta Kumar Nanda

Jiani Chen

Tanner Gilligan

Amanjit Singh Johal

Lila Fridley

Devarajan Sundararajan

Jeetendra Shashikant Dhake

An example method of entity lifecycle management in a service monitoring system includes: receiving, by a software application of a service monitoring system, a policy definition specifying an entity lifecycle management policy, wherein the entity lifecycle management policy defines management rules for a plurality of entities in a network environment, wherein each entity of the plurality of entities is represented by one of: a device, an application, a service, or a user account; identifying, by applying the entity lifecycle management policy, one or more candidate entities for retirement; identifying, as retired entities, at least a subset of the one or more candidate entities; and excluding the retired entities from a plurality of active entities, thus preventing the retired entities from interacting with other components of the service monitoring system; and determining a value of a key performance indicator (KPI) reflecting an aspect of performance of the service, wherein the KPI is defined by a search query that derives the value of the KPI from machine data associated with one or more entities of the plurality of active entities.

Entity retirement in service monitoring system

An example method of entity lifecycle management in a service monitoring system includes: receiving, by a software application of a service monitoring system, a policy definition specifying an entity lifecycle management policy, wherein the entity lifecycle management policy defines management rules for a plurality of entities in the network environment, wherein each entity of the plurality of entities is represented by one of: a device, an application, a service, or a user account; identifying, by applying the entity lifecycle management policy to a plurality of active entities, one or more candidate entities for retirement; retiring at least a subset of the one or more candidate entities; and excluding the retired entities from the plurality of active entities, thus preventing the retired entities from interacting with other components of the service monitoring system.

Entity lifecycle management in service monitoring system

Systems and methods are described for performing adaptive thresholding on key performance indicator (KPI) values using an online machine learning algorithm as the KPI values or the data from which the KPI values are derived is being ingested. For example, the system can identify outliers in a moving window of KPI values. To implement the adaptive thresholding, the system may identify seasonality and/or trend components in historical KPI values. When a new KPI value is obtained, the system may remove the identified seasonality and/or trend components from the KPI value, and determine whether the modified KPI value is an outlier using sketches or quantiles. The system can then repeat this process for each subsequently received KPI value.

Adaptive thresholding of data streamed to a data processing pipeline

An example method of entity lifecycle management in a service monitoring system includes: receiving, by a software application of a service monitoring system, a policy definition specifying an entity lifecycle management policy, wherein the entity lifecycle management policy defines management rules for a plurality of entities in the network environment, wherein each entity of the plurality of entities is represented by one of: a device, an application, a service, or a user account; identifying, by applying the entity lifecycle management policy to a plurality of active entities, one or more candidate entities for retirement; identifying, as retired entities, at least a subset of the one or more candidate entities; and excluding the retired entities from the plurality of active entities, thus preventing the retired entities from interacting with other components of the service monitoring system.

Properties associated with computing resources are received. At least a portion of the received properties is used to cluster the computing resources into one or more operating groups. At least a portion of the received properties is used to determine a recommendation of an operation schedule for at least one of the one or more operating groups. The recommendation is provided. A feedback is received in response to the recommendation.

Growing community of inventors

Fremont, CA, United States of America

Ayyappa Muthusami