Abstract:
In one embodiment, a capable node in a low power and lossy network (LLN) may monitor the authentication time for one or more nodes in the LLN. The capable node may dynamically correlate the authentication time with the location of the one or more nodes in the LLN in order to identify one or more authentication-delayed nodes. The node may then select, based on the location of the one or more authentication-delayed nodes, one or more key-delegation nodes to receive one or more network keys so that the key-delegation nodes may perform localized authentication of one or more of the authentication-delayed nodes. The capable node may then distribute the one or more network keys to the one or more key-delegation nodes.
Abstract:
In one embodiment, a committed information rate (CIR) 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 CIR 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, a message is received at a caching node in a network including an indication of the message's urgency. The message is transmitted to child nodes of the caching node, and upon transmitting the message, a retransmission timer is initiated when the message is urgent, based on the indication of the message's urgency. Then, one or more acknowledgements of receipt of the transmitted message are received from one or more of the child nodes, respectively. Upon expiration of the retransmission timer, when it is determined that one or more of the child nodes did not receive the transmitted message based on the received acknowledgements, the message is retransmitted to the child nodes.
Abstract:
In one embodiment, a request is received from a requesting node in a network to assist in distributing a task of the requesting node. Upon receiving the message, a capability to perform the task of one or more helping nodes in the network is evaluated, and a helping node of the one or more helping nodes is selected to perform the task based on the evaluated capability of the selected helping node. The distribution of the task is then authorized from the requesting node to the selected helping node.
Abstract:
In one embodiment, variables maintained by each of a plurality of Learning Machines (LMs) are determined. The LMs are hosted on a plurality of Field Area Routers (FARs) in a network, and the variables are sharable between the FARs. A plurality of correlation values defining a correlation between the variables is calculated. Then, a cluster of FARs is computed based on the plurality of correlation values, such that the clustered FARs are associated with correlated variables, and the cluster allows the clustered FARs to share their respective variables.
Abstract:
In one embodiment, a first device in a network maintains raw traffic flow information for the network. The first device provides a compressed summary of the raw traffic flow information to a second device in the network. The second device is configured to transform the compressed summary for presentation to a user interface. The first device detects an anomalous traffic flow based on an analysis of the raw traffic flow information using a machine learning-based anomaly detector. The first device provides at least a portion of the raw traffic flow information related to the anomalous traffic flow to the second device for presentation to the user interface.
Abstract:
In one embodiment, a targeted node in a computer network receives a probe generation request (PGR), and in response, generates a link-local multicast PGR (PGR-Local) carrying instructions for generating probes based on the PGR. The targeted node then transmits the PGR-Local to neighbors of the targeted node to cause one or more of the neighbors to generate and transmit probes to a collection device in the computer network according to the PGR-Local instructions. In another embodiment, a particular node in a computer network receives a link-local multicast probe generation request (PGR-Local) from a targeted node in the computer network, the targeted node having received the PGR-Local from a remote device, and determines how to generate probes based on instructions carried within the PGR-Local before sending one or more probes to a collection device in the computer network according to the PGR-Local instructions.
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, 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 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.