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.

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.

There is provided a method for generating network optimizing information including the steps of identifying system devices that are comprised in a network, collecting metrics from the identified system devices, including collecting at least one metric relating to the operation, status, capability, limitations, expandability, scalability, or performance of the system devices, assessing the collected metrics according to a predetermined assessment protocol, generating a roster of metrics of interest, such metrics of interest being a group of the collected metrics that meet a selection criteria and not including other collected metrics that do not meet the selection criteria, and presenting each of the metrics of interest in a format suitable for a network operator to take corrective actions with regard to the identified non-compliant metrics or to capitalize on the identified optimization opportunities with respect to the network.

A software designed protocol (SDP) network node includes a receiver, and a processor operatively coupled to the receiver. The receiver receives instructions, and receives packets. The processor updates a configuration of the SDP network node in accordance with the received instructions, and processes the received packets.

The present disclosure relates to an apparatus and method of altruistic scheduling based on reinforcement learning. An altruistic scheduling apparatus according to an embodiment of the present disclosure includes: an external scheduling agent for determining a basic resource share for each process based on information of a resource management system; an internal scheduling agent for determining a basic resource allocation schedule for each process based on information including the basic resource share and a resource leftover based on the basic resource allocation schedule; and a leftover scheduling agent for determining a leftover resource allocation schedule based on information including the resource leftover. According to an embodiment of the present disclosure, it may be expected that reinforcement learning will not only mitigate the diminution of fairness of an altruistic scheduler but also further improve other performance indicators such as completion time and efficiency.

The present disclosure relates to an apparatus and method of altruistic scheduling based on reinforcement learning. An altruistic scheduling apparatus according to an embodiment of the present disclosure includes: an external scheduling agent for determining a basic resource share for each process based on information of a resource management system; an internal scheduling agent for determining a basic resource allocation schedule for each process based on information including the basic resource share and a resource leftover based on the basic resource allocation schedule; and a leftover scheduling agent for determining a leftover resource allocation schedule based on information including the resource leftover. According to an embodiment of the present disclosure, it may be expected that reinforcement learning will not only mitigate the diminution of fairness of an altruistic scheduler but also further improve other performance indicators such as completion time and efficiency.

Some organizations have a deployed and functional “controllerless” EVPN VxLAN Fabric in their data centers. Eventually, however, the organization may deploy a controller within the network. In one example, this disclosure describes a method that includes configuring a controller to communicate with each of a plurality of elements in a network; determining, by the controller, an initial operational state of the network; translating, by the controller, the initial operational state of the network to an intent-based configuration; pushing, by the controller, the intent-based configuration to the network to reconfigure each of the plurality of elements in the network in a manner consistent with the intent-based configuration; determining, by the controller and after pushing the intent-based configuration, an updated operational state of the network; and comparing, by the controller, the initial operational state of the network with the updated operational state of the network.

Various embodiments are generally directed to techniques for network strengthening, such as by detecting issues with one or more network components and reconfiguring one or more upstream or downstream network components to preempt issues with the one or more upstream or downstream network components, for instance. Some embodiments are particularly directed to a tool (e.g., strengthening agent) that implements pre-scripted or dynamic hardening of up and downstream dependencies of a network component in response to an issue identified with the network component. In many embodiments, up and downstream components of a network component may be reconfigured while the issue with the network component is being addressed to preempt issues with the up and downstream components.

A system receives, from one or more subsystems, one or more predicted outcomes associated with a device. The system provides provide at least a subset of the predicted outcomes as input to a machine learning model trained to identify a set of resolution actions. The system receives, from the machine learning model, the set of resolution actions for the subset of the predicted outcomes, wherein each resolution action in the set of resolution actions is associated with a probability of resolving at least one of the predicted outcomes in the subset of predicted outcomes. The system identifies a first resolution action from the set of resolution actions, wherein the first resolution action has a highest probability of resolving the at least one of the predicted outcomes in the subset of predicted outcomes. The system provides a first instruction to execute the first resolution action.

Described is a method and system for connectivity diagnostics in communication systems. The method comprises: querying a first communication device at a first time and a second time to determine whether a second communication device is connected to the first communication device and to determine a value of an operational parameter at the first and second times; and determining the second communication device disconnected from the first communication device based on detecting the second communication device was connected to the first communication device at both the first time and the second time, and detecting the value of the operational parameter at the second time is inside a range of threshold values. In one embodiment, the method comprises determining a link is unstable for connectivity based on connection duration, number and/or pattern of connection and/or disconnection events, and/or traffic activity during connection and/or disconnection events.