Hewlett Packard Enterprise Development LP

Hewlett-packard Development Company, L.p.

Juliano Vacaro

Sebastien Tandel

David Murphy

Wagston Staehler

Thomas Da Silva Paula

Alexandre Santos Da Silva, Jr

Gabriel Rodrigo De Lima Paz

Paul Allen Bottorff

Bryan E Stiekes

Donald Fedyk

Joao Edwardo Carrion

Carlos Gomez Gallego

Rafael Anton Eichelberger

Dorian Conde Langbeck

Joao Edurado Carrion

Rafael Eichelberger

Bryan Stiekes

A method includes receiving, with a computing device, a first client request from a first client that identifies a machine learning model and a sensor. The method includes sending, with the computing device, a call to a server to apply the identified machine learning model to a set of data from the identified sensor, in response to the first client request. The method includes receiving, with the computing device, a second client request from a second client that identifies a same machine learning model and sensor as the first client request. The method includes sending, with the computing device, response data from the identified machine learning model to both the first client and the second client without sending an additional call to the server in response to the second client request.

Responding to machine learning requests from multiple clients

A device is authenticated for communication over a network based on a sensor data signature and a traffic pattern signature. The sensor data signature and the traffic pattern signature identify the device. A determination is made whether the sensor data signature corresponds to one of a plurality of recognized sensor data signatures. A determination is also made whether the traffic pattern signature of the device corresponds to one of a plurality of recognized traffic pattern signatures. The device is authenticated for communication over the network responsive to determining that the sensor data signature corresponds to one of the plurality of recognized sensor data signatures and the traffic pattern signature corresponds to one of the plurality of recognized traffic pattern signatures.

Continuous multifactor device authentication

A method includes receiving, with a computing device, a configuration file and a client request to apply a machine learning model to a set of data from a sensor. The method includes performing, with the computing device, preprocessing on the set of data from the sensor based on the configuration file to generate preprocessed data. The method includes sending, with the computing device, a call to a model server to apply the machine learning model to the preprocessed data.

Preprocessing sensor data for machine learning

Examples relate to load balancing servers. In one example, a computing device may: receive a network packet from a source device, the network packet including data specifying a value; divide the value included in the network packet by a divisor; determine, from a plurality of servers, a destination server for the network packet based on a remainder of the division; and forward the network packet to the destination server.

Server load balancing

According to examples, an apparatus may include a processor and a memory on which is stored machine readable instructions. The instructions may cause the processor to intercept a packet from a downstream service function classifier, in which the packet includes metadata that specifies an ordered set of service functions within a service function chain to be implemented on the packet, generate a correlation cookie that associates the packet with the service function chain, and encode the correlation cookie into the packet. The instructions may also cause the processor to store the correlation cookie and the metadata in a cache to correlate the correlation cookie and the metadata and send the packet with the encoded correlation cookie to the service function provider.

Service function chains using correlation cookies

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Porto Alegre, Brazil

Juliano Vacaro