Splunk Inc.

Ian Edward Torbett

Ayyappa Muthusami

Ankur Ashok Kath

Jeffrey Wen-Young Shih

Peter Wu

Tristan Antonio Fletcher

Jiani Chen

Tanner Gilligan

Kan Wu

Rehan Mulla

Bixia Yan

Lila Fridley

James Wang

Tapan Manojkumar Shah

Jeetendra Shashikant Dhake

Abhijit Bhave

Ananta Krishna Vijay Kumar Gampaa

Everett Kotler

An example method of content pack management by a service monitoring system includes: receiving a plurality of object identifiers, each object identifier referencing a corresponding object installed in an instance of a service monitoring system; performing a partial backup of the instance of a service monitoring system, wherein the partial backup comprises a plurality of objects referenced by the plurality of object identifiers; converting the partial backup into a plurality of object definitions in a predefined format; and packaging the plurality of object definitions into a content pack.

Content pack management 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 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

A method includes selecting, from content packs in a centralized content management system, a content pack to update in a data intake and query system. The content pack includes utility objects. For each utility object of at least a subset of the utility objects determining whether the utility object already exists in the data intake and query system, and loading the utility object to the data intake and query system when the utility object does not exist to obtain an updated utility object. The method further includes monitoring, by the data intake and query system, an endpoint of an endpoint type using the updated utility object.

Content pack management

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.

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Dublin, CA, United States of America

Ian Edward Torbett