Cisco Technology, Inc.

Matthias Falkner

Jerome Henry

Robert Edgar Barton

Maik Guenter Seewald

Robert E Barton

Frank Brockners

Indermeet Singh Gandhi

Thomas Michel-Ange Feltin

Novel techniques and mechanisms enable processing of heavy deep learning workloads on standard edge network devices to optimize the overall inference throughput of the network while meeting Service Level Agreement(s) (SLAs). The techniques can include receiving a deep learning model, determining a graph structure of the deep learning model including neurons organized in layers (the layers including an input layer, a plurality of hidden layers, and an output layer), assigning to a first IP subnet, at least a part of a first hidden layer of the plurality of hidden layers, assigning to a second IP subnet, at least a part of a second hidden layer of the plurality of hidden layers, and deploying the parts of the first and second hidden layers to edge devices as containerized applications with assigned IP addresses, which may be hidden from the user and/or third party application.

Hidden-layer routing for disaggregated artificial neural networks

A server, in communication with a plurality of microservices in a microservices mesh environment, obtains data about inbound communications to a first microservice and outbound communications from the first microservice of the plurality of microservices. The server analyzes the data to learn an operational behavior of the first microservice and determine a firewall rule set to be applied associated with the first microservice based on the operational behavior learned for the first microservice. The server causes a micro-firewall to be instantiated for the first microservice. The micro-firewall is configured to apply the firewall rule set to inbound communications to the first microservice and outbound communications from the first microservice.

Micro-firewalls in a microservice mesh environment

Growing community of inventors

Ottawa, Canada

Matthias Falkner