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 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.
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 triggered reboot of a field area router (FAR) of a computer network is initiated, and gathered states of the FAR are saved. The nodes in the computer network are informed of the triggered reboot, and then feedback may be collected from the nodes in response to the triggered reboot. As such, it can be determined whether to complete the triggered reboot based on the feedback, and the FAR is rebooted in response to determining to complete the triggered reboot. In another embodiment, a node receives information about the initiated triggered reboot of the FAR, and determines whether it has critical traffic. If not, the node buffers non-critical traffic and indicates positive feedback in response to the triggered reboot, but if so, then the node continues to process the critical traffic and indicates negative feedback in response to the triggered reboot.
Abstract:
In one embodiment, techniques are shown and described relating to learning machine based detection of abnormal network performance. In particular, in one embodiment, a border router receives a set of network properties xi and network performance metrics Mi from a network management server (NMS), and then intercepts xi and Mi transmitted from nodes in a computer network of the border router. As such, the border router may then build a regression function F based on xi and Mi, and can detect one or more anomalies in the intercepted xi and Mi based on the regression function F. In another embodiment, the NMS, which instructed the border router, receives the detected anomalies from the border router.
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, a device identifies available resources of a tunnel in a first software defined network. The device provides, based on the available resources, an indication that the tunnel is available to convey traffic sent by a second software defined network. The device receives, based on the indication, a request to convey traffic sent by the second software defined network via the tunnel in the first software defined network. The device configures a peering node in the first software defined network to connect the second software defined network to the tunnel to allow the traffic sent by the second software defined network to be conveyed via the tunnel.
Abstract:
In one embodiment, a service in a network computes an expected information gain associated with rerouting traffic from a first tunnel onto a backup tunnel in the network. The service initiates, based on the expected information gain, rerouting of the traffic from the first tunnel onto the backup tunnel. The service obtains performance measurements for the traffic rerouted onto the backup tunnel. The service uses the performance measurements to train a machine learning model to predict whether rerouting traffic from the first tunnel onto the backup tunnel will satisfy a service level agreement (SLA) of the traffic.
Abstract:
In one embodiment, a device obtains browser waterfall data from a web browser of a client that is used to access an online application via a network. The device obtains user feedback from the client indicative of whether a user of the client is satisfied with their experience with the online application. The device trains, using the browser waterfall data and user feedback as training data, a prediction model to predict a quality of experience metric for the online application. The device causes an adjustment to the network based on a prediction by the prediction model.
Abstract:
In one embodiment, a recommendation service of a device provides a recommended action to a client of an online application predicted to improve a quality of experience metric for the online application. The device receives feedback from the client indicative of the recommended action not being implemented by a user of the client. The device determines, based on the feedback, a reason for the recommended action not being implemented. The device updates the recommendation service based on the reason for the recommended action not being implemented.