Servicenow, Inc.

Nikhil Prashant Bendre

Baskar Jayaraman

Kannan Govindarajan

Aniruddha Madhusudan Thakur

Sriram Palapudi

Fernando Ros

Firat Karakusoglu

Harry Thomas Nelson

Colby Andrew Blakeman

Jared Laethem

Sunil Kumar

Khosrow Jian Motamedi

German Andres Bertot

Non-volatile memory may contain definitions of: (i) a plurality of message queue implementations respectively associated with different queue behaviors, the different queue behaviors specified by corresponding sets of modes, and (ii) an application programming interface (API) through which applications can access one or more message queues. One or more processors may be configured to: create a message queue of a particular message queue type, supported by a corresponding message queue implementation, by specifying a set of modes corresponding to a queue behavior; receive, from a producing application and by way of the API, one or more messages for the message queue; store the one or more messages in a data structure associated with the message queue; receive, from a consuming application and by way of the API, a request to read from the message queue; and provide a message from the message queue to the consuming application.

Message queue architecture and interface for a multi-application platform

A network system may include a plurality of trainer devices and a computing system disposed within a remote network management platform. The computing system may be configured to: receive, from a client device of a managed network, information indicating (i) training data that is to be used as basis for generating a machine learning (ML) model and (ii) a target variable to be predicted using the ML model; transmit an ML training request for reception by one of the plurality of trainer devices; provide the training data to a particular trainer device executing a particular ML trainer process that is serving the ML training request; receive, from the particular trainer device, the ML model that is generated based on the provided training data and according to the particular ML trainer process; predict the target variable using the ML model; and transmit, to the client device, information indicating the target variable.

Machine learning with distributed training

An example embodiment may include a central computational instance, as well as a plurality of computational instances that are configured to execute a software module. The central instance may be configured to deliver updates to configuration data for the software module at a plurality of release times. The embodiment may involve a computing device disposed within a particular instance of the plurality of instances transmitting a request for an out-of-band delivery of the configuration data, where the out-of-band delivery is to be made during a time other than any of the release times, receiving the configuration data from source field(s) of a central database of the central instance, and writing the configuration data to destination field(s) of a local database of the particular instance. The embodiment may also involve executing the software module according to the configuration data stored in the destination field(s).

Multi-instance architecture supporting out-of-band delivery of configuration data

Fault tolerance techniques for a plurality of nodes executing application thread groups include executing at least a portion of a first application thread group based on a delegation by a first node, wherein the first node delegates an execution of the first application thread group amongst the plurality of nodes and has a highest priority indicated by an ordered priority of the plurality of nodes. A failure of the first node can be identified based on the first node failing to respond to a message sent to it. A second node can then be identified as having a next highest priority indicated by the ordered priority such that the second node can delegate an execution of a second application thread group amongst the plurality of nodes.

System and method for resolving master node failures within node clusters

Shared machine learning

Growing community of inventors

San Diego, CA, United States of America

Nikhil Prashant Bendre