Splunk Inc.

Cisco Technology, Inc.

Matteo Merli

Karthikeyan Ramasamy

Ram Sriharsha

Sanjeev Raghavendra Kulkarni

Boyang Peng

Gaurav Chawla

Mehul Goyal

Aungon Nag Radon

Sumeet Rohatgi

Ivan Kelly

Hao Zhan

Aditya Kamble

Tyler Bakke

Rahul Merulinath

Ryan Flynn

Various implementations of the present application set forth a computer-implemented method comprising obtaining, by a low-power hub device, a first set of data published by an edge device, where the low-power hub device subscribes to at least a subset of data published by the edge device, generating, by the low-power hub device, a second set of data from the first set of data by inputting the first set of data into a machine learning (ML) model executing on the low-power hub device, and transmitting the second set of data to a remote server computer system.

Real-time machine learning at an edge of a distributed network

A process for facilitating autoscaling in a stateful system is described herein. In embodiments, a set of metrics associated with a set of components of a stateful service is obtained. The set of metrics may generally indicate a utilization or a load of each of the components of the set of components (e.g., message managers and/or data stores). Thereafter, it is determined to initiate a scaling event at the stateful service in association with the set of components of the stateful service based on at least a portion of the set of metrics attaining a metric threshold indicating a threshold value for determining whether to scale stateful service components. A scaling request can then be provided to the stateful service to initiate the scaling event at the stateful service in association with the set of components of the stateful service.

Autoscaling in a stateful system

A process for ingesting raw machine data that reduces network and data intake and query system resources is described herein. For example, instead of routing the raw machine data to an intake ingestion buffer via a load balancer, a publisher may instead route metadata to the load balancer. The load balancer can use the metadata to identify an available virtual machine in the intake ingestion buffer. The load balancer can then provide to the publisher the public IP address of the available virtual machine. The publisher can communicate with the available virtual machine using the public IP address, and the available virtual machine can identify which virtual machine owns the topic related to the raw machine data. The publisher can then transmit raw machine data to the virtual machine that owns the topic.

Load balancer bypass for direct ingestion of data into a data intake and query system

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Menlo Park, CA, United States of America

Matteo Merli