Emc IP Holding Company LLC

Dell Products Inc.

Kshitij Patel

Hao Wang

Dominique Prunier

Kevin S Labonte

Peter Beale

Michael James Barnes

Ashutosh P Nanekar

Wei Cao

Daniel A Lottero

Nicholas Chun Wei

Vijayagomathi Ramasamy

Xueyun Li

Methods, apparatus, and processor-readable storage media for efficient storage of key-value data with schema integration are provided herein. An example computer-implemented method includes obtaining a metrics data message associated with a product, wherein the metrics data message has a first format and comprises a schema version and a type of the product; identifying one of a plurality of schema definitions for the metrics data message based at least in part on the schema version and the type of the product; converting the metrics data message into a second format based on the identified schema definition, wherein the second format removes at least some redundant data from the metrics data message; and storing the converted metrics data message in a metrics database.

Efficient storage of key-value data with schema integration

A system, method, and computer-readable medium for processing in-order messages. A message broker is provided with unique message keys to performing in-order ensuring that a batch of messages are sent on the same message broker partition or data flow. A reactive generic data streaming processor provides a parallel data stream uniquely grouped to each message broker partition or data flow. In-order processing is maintained on each parallel data stream by using downstream publishers that preserve strict order in each data stream. A terminal sync message with a unique message key to identifies end of in-order streaming a message group.

System and method to support in-order message processing with reactive microservices

Methods, apparatus, and processor-readable storage media for automated stateful counter aggregation of device data are provided herein. An example computer-implemented method includes obtaining historical aggregate counter data and historical individual member counter data associated with a variable set of device members and a given temporal period; computing one or more stateful aggregate counter data values attributed to at least a portion of the variable set of device members for a given temporal value by applying at least one stateful counter aggregation algorithm to the obtained data; and performing one or more automated actions based at least in part on the one or more computed stateful aggregate counter data values.

Automated stateful counter aggregation of device data

Techniques for performing application scaling may include: receiving a plurality of observed queue depth values for a queue, wherein the queue includes data packets to be processed by a first set of one or more applications of a first application type; determining, in accordance with the observed queue depth values and scaling configuration parameters for the first application type, whether to modify the number of applications currently in the first set, wherein the scaling configuration parameters includes a scaling threshold; and responsive to determining to modify the number of application currently in the first set, performing first processing that modifies the number of applications currently in the first set. Modifying the number of applications in the first set may include scaling up or scaling down the number of instances in the first set.

Dynamic application scaling

Growing community of inventors

Cary, NC, United States of America

Kshitij Patel