Abstract: In one embodiment, a supervisory device for a software defined networking (SDN) fabric predicts characteristics of a new traffic flow to be admitted to the fabric, based on a set of initial packets of the flow. The supervisory device predicts an impact of admitting the flow to the SDN fabric, using a heatmap-based saturation model for the SDN fabric. The supervisory device admits the flow to the SDN fabric, based on the predicted impact. The supervisory device uses reinforcement learning to adjust one or more call admission control (CAC) parameters of the SDN fabric, based on captured telemetry data regarding the admitted flow.

Abstract: Disclosed are systems, methods, and computer-readable media for integrating deterministic packet transmissions scheduling of short range local area networks (e.g., 6TiSCH networks) with deterministic packet transmission scheduling for wireless networks such as LTE/4G/5G networks. In one aspect, a wireless communication network includes a plurality of first nodes configured to communicate using a first communication protocol; and a second node configured to communicate with the plurality of first nodes using the first communication protocol and configured to communicate with a third node using a second communication protocol, the second node being further configured to map corresponding transmission schedule of the first communication protocol to a second transmission schedule for the second communication protocol.

Abstract: In one embodiment, a device configures a plurality of subinterfaces for each of a plurality of physical ports of a software defined network (SDN). The device allocates a fixed amount of bandwidth to each of the subinterfaces. The device forms a plurality of midlays for the SDN by assigning subsets of the plurality of subinterfaces to each of the midlays. The device assigns a network slice to one or more of the midlays, based on a bandwidth requirement of the network slice.

Abstract: In one embodiment, a device in a network receives a path computation agent configured to determine a path in the network that satisfies an objective function. The device executes the path computation agent to update state information regarding the network maintained by the path computation agent. The device selects a neighbor of the device in the network to execute the path computation agent based on the updated state information regarding the network. The device instructs the selected neighbor to execute the path computation agent with the updated state information regarding the network. The device unloads the path computation agent from the device after selecting the neighbor of the device to execute the path computation agent.

Abstract: In one embodiment, an elimination point device in a network obtains a master secret from a network controller. The elimination point device assesses, using the master secret, whether an incoming packet received by the elimination point device from a redundant path between the elimination point device and a replication point device in the network includes a valid message integrity check (MIC). The elimination point device determines whether the incoming packet was injected maliciously into the redundant path, based on the assessment of the incoming packet. The elimination point device initiates performance of a mitigation action in the network, when the elimination point device determines that the incoming packet was injected maliciously into the redundant path.

Abstract: In one embodiment, a supervisory device in a network forms a virtual access point (VAP) for a node in the network. A plurality of access points (APs) in the network are mapped to the VAP as part of a VAP mapping and the node treats the APs in the VAP mapping as a single AP for purposes of communicating with the network. The supervisory device determines a traffic type of traffic associated with the node. The supervisory device assigns the node to a selected wireless channel based in part on the traffic type of the traffic associated with the node. The supervisory device controls the VAP to use the channel assigned to the node.

Abstract: In one embodiment, a supervisory device in a network assigns different access points in the network to different access point groupings. Each of the different access point groupings uses a different network path to communicate with a given endpoint in the network. The supervisory device selects at least one of the access points in each of the different access point groupings for mapping to a virtual access point (VAP) for a node in the network as part of a VAP mapping. The supervisory device instructs the selected access points to form a VAP for the node. The node treats the access points in the VAP mapping as a single access point for purposes of communicating with the network.

Abstract: In one embodiment, a supervisory device for a software defined networking (SDN) fabric predicts characteristics of a new traffic flow to be admitted to the fabric, based on a set of initial packets of the flow. The supervisory device predicts an impact of admitting the flow to the SDN fabric, using a heatmap-based saturation model for the SDN fabric. The supervisory device admits the flow to the SDN fabric, based on the predicted impact. The supervisory device uses reinforcement learning to adjust one or more call admission control (CAC) parameters of the SDN fabric, based on captured telemetry data regarding the admitted flow.

Abstract: In one embodiment, a supervisory device for a software defined networking (SDN) fabric predicts a failure in the SDN fabric using a machine learning-based failure prediction model. The supervisory device identifies a plurality of traffic flows having associated leaves in the SDN fabric that would be affected by the predicted failure. The supervisory device selects a subset of the identified plurality of traffic flows and their associated leaves. The supervisory device disaggregates routes for the selected subset of traffic flows and their associated leaves, to avoid the predicted failure.

Abstract: In one embodiment, a supervisory service receives a registration message broadcast by a first vehicle and captured by a RSU in the network of RSUs. The supervisory service registers the first vehicle by validating a signature of the registration message without registering a media access control (MAC) address of the first vehicle and without causing to send a registration response to the first vehicle. The supervisory service receives a message broadcast by a second vehicle addressed to the first vehicle and captured by at least one RSU in the network of RSUs. The supervisory service selects one or more RSUs in the network of RSUs to re-broadcast the message. The supervisory service controls the one or more RSUs to re-broadcast the message.

Abstract: In one embodiment, a particular device along a path in a deterministic network receives a first packet sent from a source towards a destination via the path. The particular device sends the first packet to a next hop device along the path, according to a deterministic schedule associated with the first packet. The particular device determines, after sending the first packet, an action to be performed on the first packet. The particular device then sends a second packet to the next hop device indicative of the determined action. The second packet causes another device along the path to perform the action on the first packet.

Abstract: A first address resolution request may be received by a first access switch from a first device and the address resolution request may be resolved by the first access switch with a central database of a network. Then a second address resolution request may be sent to a sensor by the first access switch in response to resolving the first address resolution request. An address resolution response may then be sent by the sensor to the first device in response to the sensor determining that the first device is a bad endpoint. A session may then be established between the sensor and the first device in response to the sensor sending the address resolution response. The first device may then be prompted by the sensor via the established session to resolve issues that lead the sensor to determine that the first device is a bad endpoint.

Abstract: In one embodiment, a first actuator in a network of sensors and actuators executes a walker agent configured to adjust an actuation setting of the first actuator. The actuation setting controls an area of coverage of the first actuator when actuated. The executing agent on the first actuator receives one or more sensor measurements from one or more of the sensors that are in communication range of the first actuator. The executing agent also controls, based on the received one or more sensor measurements, the area of coverage of the first actuator by adjusting its actuation setting, in an attempt to optimize coverage of the sensors in the network by the areas of coverage of the actuators. The first actuator unloads the executing walker agent after adjusting the actuation setting of the first actuator and propagates the agent to another one of the actuators in the network for execution.

Abstract: In one embodiment, a supervisory device in a network forms a virtual access point (VAP) for a node in the network. A set of access points (APs) in the network are mapped to the VAP as part of a VAP mapping and the node treats the APs in the VAP mapping as a single AP for purposes of communicating with the network. The supervisory device receives measurements from the APs in the VAP mapping regarding communications associated with the node. The supervisory device identifies a movement of the node based on the received measurements from the APs in the VAP mapping. The supervisory device adjusts the set of APs in the VAP mapping based on the identified movement of the node.

Abstract: In one embodiment, a device in a network receives a query walker agent configured to query information from a distributed set of devices in the network based on a query. The device executes the query walker agent to identify the query. The device updates state information of the executing query walker agent using local information from the device and based on the query. The device unloads the executing query walker agent after updating the state information. The device propagates the query walker agent with the updated state information to one or more of the distributed set of devices in the network, when the updated state information does not fully answer the query.

Abstract: In one embodiment, a cloud-based service instructs one or more networking devices in a local area network (LAN) to form a virtual network overlay in the LAN that redirects traffic associated with a particular node in the LAN to the service. The service receives multicast or broadcast traffic sent by the particular node in the LAN and redirected to the service via the virtual network overlay. The service identifies a group of nodes in the network that are to receive the traffic sent by the particular node, based in part by profiling the traffic associated with the particular node. The service sends the traffic sent by the particular node to at least one networking device in the LAN with an indication of the identified group of nodes in the network that are to receive the traffic sent by the particular node. The at least one networking device forwards the traffic sent by the particular node to the nodes in the identified group.

Abstract: In one embodiment, a server instructs one or more networking devices in a local area network (LAN) to form a virtual network overlay in the LAN that redirects traffic associated with a particular node in the LAN to the server. The server receives the redirected traffic associated with the particular node. The server trains a machine learning-based behavioral model for the particular node based on the redirected traffic. The server controls whether a particular redirected traffic flow associated with the node in the LAN is sent to a destination of the traffic flow using the trained behavioral model.

Abstract: In one embodiment, a device in a network identifies a node in the network that is not synchronized to a network time synchronization mechanism. The device determines a scheduled reception time for a particular deterministic traffic flow at which the device is to receive the traffic flow from the node. The device sends, prior to the scheduled reception time, a request to the node for the particular deterministic traffic flow. The request identifies the particular deterministic traffic flow and causes the node to send the traffic flow to the device. The device receives the particular deterministic traffic flow from the node at the scheduled reception time.

Abstract: A particular fat tree network node stores default routing information indicating that the particular fat tree network node can reach a plurality of parent fat tree network nodes of the particular fat tree network node. The particular fat tree network node obtains, from a first parent fat tree network node of the plurality of parent fat tree network nodes, a negative disaggregation advertisement indicating that the first parent fat tree network node cannot reach a specific destination. The particular fat tree network node determines whether the first parent fat tree network node is the only parent fat tree network node of the plurality of parent fat tree network nodes that cannot reach the specific destination. If so, the particular fat tree network node installs supplemental routing information indicating that every parent fat tree network node except the first parent fat tree network node can reach the specific destination.

Abstract: In one embodiment, a device receives data indicative of packet arrival times at a plurality of nodes along a path in a deterministic network. The device compares the packet arrival times to their corresponding scheduled delivery intervals in a deterministic communication schedule used by the nodes along the path. The device detects, using a machine learning-based anomaly detector, a time synchronization anomaly based on the comparisons between the packet arrival times and their scheduled delivery intervals. The device causes performance of a mitigation action in the network based on the detected time synchronization anomaly.