Disclosed is an improved approach for managing floating/virtual IP addresses in a virtualization system. Where a bare metal cloud provider does not provide adequate facilities to implement broadcast operations, the approach would capture broadcast packets, and from the captured packets, generate calls to the cloud provider to implement configuration changes to reflect the changes desired by the broadcast packets.

Disclosed is an improved approach for managing floating/virtual IP addresses in a virtualization system. Where a bare metal cloud provider does not provide adequate facilities to implement broadcast operations, the approach would capture broadcast packets, and from the captured packets, generate calls to the cloud provider to implement configuration changes to reflect the changes desired by the broadcast packets.

Training and enhancement of neural network models, such as from private data, are described. A slave device receives a version of a neural network model from a master. The slave accesses a local and/or private data source and uses the data to perform optimization of the neural network model. This can be done such as by computing gradients or performing knowledge distillation to locally train an enhanced second version of the model. The slave sends the gradients or enhanced neural network model to a master. The master may use the gradient or second version of the model to improve a master model.

Training and enhancement of neural network models, such as from private data, are described. A slave device receives a version of a neural network model from a master. The slave accesses a local and/or private data source and uses the data to perform optimization of the neural network model. This can be done such as by computing gradients or performing knowledge distillation to locally train an enhanced second version of the model. The slave sends the gradients or enhanced neural network model to a master. The master may use the gradient or second version of the model to improve a master model.

In general, embodiments described herein relate to methods and systems for automating the configuration of network devices. More specifically, embodiments of the invention relate to using configuration commands that specify protocol-specified relationships in order to generate granular (or specific) filtering rules (also referred to as rules). The rules are subsequently programmed into the network device.

The Distributed Software Defined Network (dSDN) disclosed herein is an end-to-end architecture that enables secure and flexible programmability across a network with full lifecycle management of services and infrastructure applications (fxDeviceApp). The dSDN also harmonizes application deployment across the network independent of the hardware vendor. As a result, the dSDN simplifies the network deployment lifecycle from concept to design to implementation to decommissioning.

The Distributed Software Defined Network (dSDN) disclosed herein is an end-to-end architecture that enables secure and flexible programmability across a network with full lifecycle management of services and infrastructure applications (fxDeviceApp). The dSDN also harmonizes application deployment across the network independent of the hardware vendor. As a result, the dSDN simplifies the network deployment lifecycle from concept to design to implementation to decommissioning.

Technologies for allocating resources of managed nodes to workloads to balance multiple resource allocation objectives include an orchestrator server to receive resource allocation objective data indicative of multiple resource allocation objectives to be satisfied. The orchestrator server is additionally to determine an initial assignment of a set of workloads among the managed nodes and receive telemetry data from the managed nodes. The orchestrator server is further to determine, as a function of the telemetry data and the resource allocation objective data, an adjustment to the assignment of the workloads to increase an achievement of at least one of the resource allocation objectives without decreasing an achievement of another of the resource allocation objectives, and apply the adjustments to the assignments of the workloads among the managed nodes as the workloads are performed. Other embodiments are also described and claimed.

A scheduling scheme configuration method includes performing state verification on a plurality of operation dimensions involved in generating a scheduling scheme, and, in response to one or more of the operation dimensions being abnormal, removing the one or more abnormal operation dimensions to generate a new scheduling scheme.

A scheduling scheme configuration method includes performing state verification on a plurality of operation dimensions involved in generating a scheduling scheme, and, in response to one or more of the operation dimensions being abnormal, removing the one or more abnormal operation dimensions to generate a new scheduling scheme.