Abstract:
In one embodiment, a device receives a destination unreachable message originated by a particular node along a first source route, the message carrying an encapsulated packet as received by the particular node. In response, the device may determine a failed link along the first source route based on a tunnel header and the particular node. Once determining an alternate source route without the failed link, the device may re-encapsulate and re-transmit the original packet on an alternate source route with a new tunnel header indicating the alternate source route (e.g., and a new hop limit count for the tunnel header and an adjusted hop limit count in the original packet).
Abstract:
In one embodiment, a source node monitors a quality of a primary link, and forwards one or more duplicate copies of a packet in response to poor quality of the primary link. Specifically, forwarding generally comprises transmitting a first copy of the packet on the primary link with an indication of duplicate copies, and transmitting a second copy of the packet on a backup link with an indication of duplicate copies. In another embodiment, an intermediate node receives a first copy of a packet with an indication of duplicate copies, and stores an identifier of the first copy of the packet in response to the indication. Upon receiving a second copy of the packet with the indication of duplicate copies, the node determines whether the identifier of the second copy matches the stored identifier of the first copy, such that in response to a match, the second copy is dropped.
Abstract:
In one embodiment, a traffic engineering (TE) label switched path (LSP) is established between a head-end node in a local domain and a tail-end node in a remote domain. The TE-LSP spans one or more intervening domains located between the local domain and the remote domain. The head-end node sends a routing information request over the TE-LSP to a target node on the TE-LSP that is in the remote domain. The head end node receives routing information from the target node. The received routing information includes a list of address prefixes reachable by the target node. The head end node uses the received routing information to calculate routes reachable via the TE-LSP to the target node. The calculated routes have a next-hop interface set to be the TE-LSP. The calculated routes are inserted into a routing table of the head-end node.
Abstract:
In one implementation, a device obtains a natural language-based description of a network via a user interface. The device generates, based on the natural language-based description, network configuration parameters for the network using a generative model. The device conducts a simulation of traffic in the network using the network configuration parameters, to obtain telemetry data. The device uses the telemetry data to train a machine learning model to perform network analytics.
Abstract:
In one embodiment, a device receives, at a first large language model executed by a device, textual input from a user of a network regarding a networking issue in the network. The device issues, by the first large language model and to a second large language model, one or more questions regarding the network based on the textual input. The device receives, at the first large language model and from the second large language model, one or more answers to the one or more questions. The device generates, by the first large language model, a textual response to the textual input for presentation to the user.
Abstract:
In one embodiment, a device obtains a plurality of characteristics of different portions of a network for which a predictive networking engine is available. The device provides the plurality of characteristics of the different portions of a network to a user interface. The device receives, via the user interface, a set of one or more constraints to limit recommendations by the predictive networking engine for a selected portion of the network from among the different portions of the network. The device configures the predictive networking engine to prevent it from generating recommendations for the selected portion of the network according to the set of one or more constraints.
Abstract:
In one embodiment, a device identifies a timeseries motif present in a plurality of timeseries of performance metrics for a plurality of paths in a network. The device retrieves, based on the timeseries motif, device-level telemetry data from networking devices along the plurality of paths. The device determines a root cause of the timeseries motif by correlating the timeseries motif with the device-level telemetry data. The device provides an indication of the timeseries motif and its root cause for display by a user interface.
Abstract:
In one embodiment, a device provides, to a user interface, a timeseries for display of a probability over time of a network path violating a service level agreement (SLA) associated with an online application. The device receives, from the user interface, a plurality of thresholds for the timeseries that define periods of time during which application experience of the online application is believed to be degraded. The device trains, based on the plurality of thresholds, a machine learning model to predict when the application experience of the online application will be degraded. The device causes a predictive routing engine to reroute traffic of the online application based on a prediction by the machine learning model that the application experience of the online application will be degraded.
Abstract:
In one embodiment, a device obtains a first set of measurements of a path metric for a path in a network that are measured using periodic probing of the path. The device obtains a second set of measurements of the path metric for the path that are measured using fine-grained probing of the path at a higher frequency than that of the periodic probing. The device generates a predictive model that predicts values of the path metric, based on the first set of measurements and on the second set of measurements. The device causes, based on a value of the path metric predicted by the predictive model, traffic to be rerouted from the path to another path in the network.
Abstract:
In one embodiment, a device obtains an indication of a network event predicted by a routing engine for a network. The device initiates monitoring of one or more network paths associated with the network event, to determine one or more states of the network. The device makes a comparison between the one or more states of the network and a set of one or more constraints. The device provides a prediction cancelation notification to the routing engine, based on the comparison.