Approaches presented herein enable deploying a distributed machine learning framework in an edge computing environment. More specifically, a status of a connection between a computing system and an edge node of a plurality of edge nodes is monitored. At least one server node and a group of worker nodes from the plurality of edge nodes are identified based on the status. A path for distributing the training data to the worker nodes is determined based on the status. The training data from the edge node to the worker nodes is distributed via the path.

Techniques for data source driven expected network policy control are described. A policy enforcement service receives, from a compute instance in a virtual network implemented within a service provider system, a request to access data. The policy enforcement service determines that a virtual network security condition of a policy statement is not satisfied. The policy statement was configured by a user for use in controlling access to the data. The virtual network security condition defines a condition of the virtual network that is to be met. The policy enforcement service performs one or more security actions in response to the determination that the virtual network security condition of the policy statement is not satisfied.

The present disclosure relates to systems and methods for providing cloud-based services securely to on-premises networks or other infrastructure. More particularly, the present disclosure relates to systems and methods for enriching first-party data (e.g., data collected directly by an on-premises server) stored within on-premises networks by enabling the on-premises networks to retrieve and process third-party data stored on cloud-based networks. As a technical benefit, cloud-based services can be performed on the first-party data within the on-premises networks.

The present disclosure relates to systems and methods for providing cloud-based services securely to on-premises networks or other infrastructure. More particularly, the present disclosure relates to systems and methods for enriching first-party data (e.g., data collected directly by an on-premises server) stored within on-premises networks by enabling the on-premises networks to retrieve and process third-party data stored on cloud-based networks. As a technical benefit, cloud-based services can be performed on the first-party data within the on-premises networks.

Disclosed are systems, methods, and computer-readable media for assuring tenant forwarding in a network environment. Network assurance can be determined in layer 1, layer 2 and layer 3 of the networked environment including, internal-internal (e.g., inter-fabric) forwarding and internal-external (e.g., outside the fabric) forwarding in the networked environment. The network assurance can be performed using logical configurations, software configurations and/or hardware configurations.

Disclosed are systems, methods, and computer-readable media for assuring tenant forwarding in a network environment. Network assurance can be determined in layer 1, layer 2 and layer 3 of the networked environment including, internal-internal (e.g., inter-fabric) forwarding and internal-external (e.g., outside the fabric) forwarding in the networked environment. The network assurance can be performed using logical configurations, software configurations and/or hardware configurations.

Described are platforms, systems, and methods for resource fairness enforcement. In one aspect, a programmable input output (IO) device comprises a memory unit, the memory unit having instructions stored thereon which, when executed by the programmable IO device, cause the programmable IO device to perform operations comprising: receiving an input from a logical interface (LIF); determining, by at least one meter, a metric regarding at least one resource used during a processing of the input through a programmable pipeline; and regulating additional input received from the LIF based on the metric and a threshold for the at least one resource.

A cloud management server and method for performing automatic placement of clients in a distributed computer system uses a list of compatible clusters to select an affinity cluster to place the clients associated with an affinity constraint. As part of the placement method, a cluster that cannot satisfy any anti-affinity constraint associated with the clients and the affinity constrain is removed from the list of compatible clusters. After the affinity cluster has been selected, at least one cluster in the distributed computer system is also selected to place clients associated with an anti-affinity constraint.

A cloud management server and method for performing automatic placement of clients in a distributed computer system uses a list of compatible clusters to select an affinity cluster to place the clients associated with an affinity constraint. As part of the placement method, a cluster that cannot satisfy any anti-affinity constraint associated with the clients and the affinity constrain is removed from the list of compatible clusters. After the affinity cluster has been selected, at least one cluster in the distributed computer system is also selected to place clients associated with an anti-affinity constraint.

Some embodiments provide a method for configuring a logical middlebox in a hosting system that includes a set of nodes. The logical middlebox is part of a logical network that includes a set of logical forwarding elements that connect a set of end machines. The method receives a set of configuration data for the logical middlebox. The method uses a stored set of tables describing physical locations of the end machines to identify a set of nodes at which to implement the logical middlebox. The method provides the logical middlebox configuration for distribution to the identified nodes.