Splunk Inc.

Pradeep Baliganapalli Nagaraju

Adam Jamison Oliner

Erick Anthony Dean

Jiahan Wang

Brian Matthew Gilmore

Steve Yu Zhang

Dharmalingam Madheswaran

Joseph Timko

Richa Mehta

Prashant Kumar

Maurice Stanley Barnum

Christopher Kellogg

A process for providing requests to a management application in a multi-tenant environment is described herein. In embodiments, a broker client is deployed within a tenant execution environment executed by a server computer system. In embodiments, the broker client is configured to communicate with a broker responsible for managing the tenant execution environment based on configuration information. Furthermore, in various embodiments, request to perform operations associated with the tenant execution environment are transmitted to the broker client over a connection and the broker client provides the request to the broker for execution.

Controlling access in multi-tenant environments

A process for facilitating downscaling of datastores (e.g., in a stateful system) is described herein. In embodiments, a set of metrics associated with a set of data stores of a stateful service is obtained. The set of metrics may indicate a utilization of each of the data stores of the set of data stores. Based on the set of metrics indicating underutilization associated with at least a portion of the set of data stores, a determination is made to initiate a downscaling of the set of data stores. Thereafter, a downscaler is deployed to perform downscaling operations to downscale the set of data stores. The downscaler communicates with a first data store to replicate data of the first data store onto a second data store. Based on identifying that the downscaler has completed the downscaling operations to downscale the set of data stores, the downscaler is terminated.

Automated downscaling of data stores

Disclosed is a technique that can be performed by a server computer system. The technique can include obtaining data from each of multiple endpoint devices to form global data. The global data can be generated by the endpoint devices in accordance with local instructions in each of the endpoint devices. The technique further includes generating global instructions based on the global data and sending the global instructions to a particular endpoint device. The global instructions configure the particular endpoint device to perform a data analytic operation that analyzes events. The events can include raw data generated by a sensor of the particular endpoint device.

Server-side operations for edge analytics

Disclosed is a technique that can be performed by an electronic device. The electronic device can generate time-stamped events, extract training data from the time-stamped events, and send the training data over a network to a remote computer. The electronic device can receive model data generated by the remote computer from the training data by use of a machine learning process, update a local model of the electronic device based on the received model data, and generate an output by processing locally sourced data of the electronic device with the updated local model.

Data analytics in edge devices

An example method of updating a client dashboarding component of an asset monitoring and reporting system comprises: identifying an update of a client dashboarding component of an asset monitoring and reporting system (AMRS), the client dashboarding component comprising one or more dynamic elements, each dynamic element associated with an asset node; receiving one or more search queries, each search query corresponding to a dynamic element of the one or more dynamic elements; modifying one or more dynamic elements of the client dashboarding component in accordance with the one or more search queries; and updating the client dashboarding component to reflect metric values associated with the modified dynamic elements.

Updating client dashboarding component of an asset monitoring and reporting system

Disclosed is a technique that can be performed by a server computer system. The technique can include executing a machine learning process to generate a machine learning model based on global data collected from one or more electronic devices, wherein the machine learning model is described by model data. The technique can further include encapsulating the model data in a markup language document. The technique can further include sending, over a network, the markup language document to at least one electronic device of the one or more electronic devices to cause the at least one electronic device to update a local device machine learning model.

Transmitting machine learning models to edge devices for edge analytics

An asset monitoring and reporting system (AMRS) implements decoupled update cycle and disparate search frequency dispatch for dynamic elements of an asset monitoring and reporting system. The AMRS identifies occurrence of an update to a visualization of a client dashboarding component of an AMRS, the visualization comprising dynamic elements and corresponding dynamic element searches that are each associated with a search query to be submitted for execution to obtain a value of a metric of an asset node associated with a respective dynamic element. The AMRS further sends a request indicative of the dynamic elements to a server component of the AMRS, receives dynamic element objects for the dynamic elements, the dynamic element objects specifying search queries corresponding to the dynamic elements, modifies dynamic element searches of the dashboarding component in accordance with the search queries, and stores a definition of the visualization as control information.

Decoupled update cycle and disparate search frequency dispatch for dynamic elements of an asset monitoring and reporting system

Disclosed is a technique that can be performed by an electronic device. The technique can include generating timestamped events, where the timestamped events include raw data generated by electronic device. The technique can further include obtaining results by performing a operation on the timestamped events, in accordance with instructions. The technique can further include sending the results or indicia thereof over a network to a server computer system, and receiving back new instructions generated by the server computer system based on the sent results. Lastly, the technique can include performing a new operation on timestamped events including raw data generated based by the electronic device, where the new operation can be performed in accordance with the new instructions to obtain new results.

Analytics for edge devices

Disclosed is a technique that can be performed by an electronic device. The technique can include generating raw data based on inputs to the electronic device, and sending the raw data or data items over a network to a server computer system. The sent raw data or the data items can include training data. The technique can further include receiving global model data from the server computer system over the network. The global model data may have been derived from the training data in accordance with a machine learning process. The technique can further include generating an updated local model by updating a local model associated with the electronic device based on the received global model data, and processing local data based on the updated local model to generate output data. The local data can include raw data or data items generated based on inputs to the electronic device.

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San Francisco, CA, United States of America

Pradeep Baliganapalli Nagaraju