Abstract: In one embodiment, a device in a network identifies a set of one or more destination addresses for which traffic shaping is to be performed by controlling the data rate at which traffic is sent to the one or more destination addresses. The device sends the traffic to one of the destination addresses along a communication path in the network and at a particular data rate. The device identifies a change in a performance characteristic for the communication path. The device adjusts the data rate at which the traffic is sent along the communication path, in response to identifying the change in the performance characteristic for the communication path.

Abstract: In one embodiment, a management system determines respective capability information of machine learning systems, the capability information including at least an action the respective machine learning system is configured to perform. The management system receives, for each of the machine learning systems, respective performance scoring information associated with the respective action, and computes a degree of freedom for each machine learning system to perform the respective action based on the performance scoring information. Accordingly, the management system then specifies the respective degree of freedom to the machine learning systems. In one embodiment, the management system comprises a management device that computes a respective trust level for the machine learning systems based on receiving the respective performance scoring feedback, and a policy engine that computes the degree of freedom based on receiving the trust level.

Abstract: In one embodiment, a device in a network monitors performance data for a first predictive model. The first predictive model is used to make proactive decisions in the network. The device maintains a supervisory model based on the monitored performance data for the first predictive model. The device identifies a time period during which the supervisory model predicts that the first predictive model will perform poorly. The device causes a switchover from the first predictive model to a second predictive model at a point in time associated with the time period, in response to identifying the time period.

Abstract: In one embodiment, a device in a network receives an indication of a traffic shaping rate adjustment by a node due to a network condition. The device identifies a set of network nodes that are associated with the network condition. The device detects a traffic shaping rules violation by an offending node in the set of network nodes. The device sends an instruction that causes the offending node to use a different traffic shaping rate.

Abstract: In one embodiment, a device in a network receives a switchover policy for a particular type of traffic in the network. The device determines a predicted effect of directing a traffic flow of the particular type of traffic from a first path in the network to a second path in the network. The device determines whether the predicted effect of directing the traffic flow to the second path would violate the switchover policy. The device causes the traffic flow to be routed via the second path in the network, based on a determination that the predicted effect of directing the traffic flow to the second path would not violate the switchover policy for the particular type of traffic.

Abstract: In one embodiment, a device in a network receives data indicative of traffic characteristics of traffic associated with a particular application. The device identifies one or more paths in the network via which the traffic associated with the particular application was sent, based on the traffic characteristics. The device determines a probing schedule based on the traffic characteristics. The probing schedule simulates the traffic associated with the particular application. The device sends probes along the one or more identified paths according to the determined probing schedule.

Abstract: In one embodiment, a network device routes traffic along a network path and receives a performance threshold crossing alert regarding performance of the network path. The network device detects that the performance threshold crossing alert is part of a potential network attack by analyzing, by the device, the performance threshold crossing alert. The network device also provides a notification of the detected network attack.

Abstract: In one embodiment, a management device may determine whether user traffic in a computer network is suffering from insufficient network resources. In response to user traffic suffering from insufficient network resources, the device may then trigger the computer network to reduce control plane traffic. In another embodiment, a network device may transmit control plane traffic into a computer network at a first rate. In response to receiving instructions to reduce control plane traffic due to user traffic suffering from insufficient network resources, the device may then transmit control plane traffic into the computer network at a reduced second rate.

Abstract: In one embodiment, a capable node in a computer network may host a path computation element, receive one or more neighborhood discovery messages including neighborhood information from a plurality of nodes in the computer network, and compute a minimum spanning tree (MinTree) for the computer network based on the neighborhood information. The MinTree may divide the plurality of nodes in the computer network into a first subset of routing nodes and a second subset of host nodes. The first subset of routing nodes may form one or more interconnected paths of routing nodes within the MinTree, and each host node within the second subset of host nodes may be located within one hop of at least one routing node. The capable node may then communicate a MinTree message to the plurality of nodes in the computer network to build the MinTree by enabling routing on each routing node.

Abstract: In one embodiment, a device in a computer network determines one or more tunnels affected by a downstream fault in the computer network, and determines one or more common Ethernet segments of the device used by the affected tunnels. As such, the device generates, for each of the one or more common Ethernet segments, a respective fault message aggregating tunnel information of each of one or more particular affected tunnels on the corresponding common Ethernet segment, and sends each respective fault message with aggregated tunnel information over a selected tunnel of the one or more particular affected tunnels on the corresponding common Ethernet segment.

Abstract: In one embodiment, a network device receives metrics regarding a path in the network. A predictive model is generated using the received metrics and is operable to predict available bandwidth along the path for a particular type of traffic. A determination is made as to whether a confidence score for the predictive model is below a confidence threshold associated with the particular type of traffic. The device obtains additional data regarding the path based on a determination that the confidence score is below the confidence threshold. The predictive model is updated using the additional data regarding the path.

Abstract: In one embodiment, data is received at a device regarding a network-monitoring process in which one or more nodes in a network export network metrics to one or more collector nodes. A change to the network-monitoring process is determined based on the received data. The device also adjusts the network-monitoring process to implement the determined change.

Abstract: In one embodiment, a committed information rate (CR) prediction is received from a machine learning model that corresponds to a predicted average traffic rate supported by a network connection. A traffic shaping strategy is adjusted based on the CR prediction. A rate at which data is communicated over the network connection may be based on the traffic shaping policy. The effects of the adjusted traffic shaping strategy are also monitored. Feedback is further provided to the machine learning model based on the monitored effects of the adjusted traffic shaping strategy.

Abstract: In one embodiment, network traffic data is received regarding traffic flowing through one or more routers in a network. A future traffic profile through the one or more routers is predicted by modeling the network traffic data. Network condition data for the network is received and future network performance is predicted by modeling the network condition data. A behavior of the network is adjusted based on the predicted future traffic profile and on the predicted network performance.

Abstract: In one embodiment, a plurality of paths in a network from a source device to a destination device is identified. A predicted performance for packet delivery along a primary path from the plurality of paths is determined. The predicted performance for packet delivery along the primary path is then compared to a performance threshold. Traffic sent along the primary path may be duplicated onto a backup path selected from the plurality of paths based on a determination that the predicted performance along the primary path is below the performance threshold.

Abstract: In one embodiment, a time period is identified in which probe packets are to be sent along a path in a network based on predicted user traffic along the path. The probe packets are then sent during the identified time period along the path. Conditions of the network path are monitored during the time period. The rate at which the packets are sent during the time period is dynamically adjusted based on the monitored conditions. Results of the monitored conditions are collected, to determine an available bandwidth limit along the path.

Abstract: In one embodiment, a primary root node may detect one or more neighboring root nodes based on information received from a first-hop node and may select a backup root node among the neighboring root nodes. Once selected, the backup root node may send the primary root node a networking identification and a corresponding group mesh key which the primary root node may forward to the first-hop nodes to cause the first-hop nodes to migrate to the backup root node when connectivity to the primary root node fails. In addition, the first-hop root nodes may migrate back to the primary root node when connectivity to the primary root node is restored.

Abstract: In one embodiment, a message instructing a particular node to act as a heartbeat relay agent is received at the particular node in a network. The particular node is selected to receive the message based on a centrality of the particular node. Heartbeat messages are then collected from child nodes of the particular node in the network. Based on the collected heartbeat messages, a heartbeat report is generated, and the report is transmitted to a collecting node in the network.

Abstract: In one embodiment, a request to make a prediction regarding one or more service level agreements (SLAs) in a network is received. A network traffic parameter and an SLA requirement associated with the network traffic parameter according to the one or more SLAs are also determined. In addition, a performance metric associated with traffic in the network that corresponds to the determined network traffic parameter is estimated. It may then be predicted whether the SLA requirement would be satisfied based on the estimated performance metric.

Abstract: In one embodiment, a routing topology of a network including nodes interconnected by communication links is determined, and activity in the network is monitored to determine a normal behavior of the communication links. Weak communication links in the network that deviate from the determined normal behavior are detected, and it is then determined whether the weak communication links are spatially correlated based on the determined topology of the network. In response to the weak communication links being spatially correlated, a region of the network affected by the weak communication links is identified as a dark zone that is to be avoided when routing data packets in the network.