Abstract:
In one embodiment, a routing topology of a network including nodes interconnected by communication links is determined. Important nodes in the network which are of relative importance are determined based on their location in the determined routing topology. Also, one or more request messages are sent causing the important nodes to gather local network metrics. Then, in response to the one or more request messages, one or more response messages including the network metrics gathered by each important node are received.
Abstract:
In one embodiment, one or more reporting nodes are selected to report network metrics in a network. From a monitoring node in the network, a trigger message is sent to the one or more reporting nodes. The trigger message may trigger the one or more reporting nodes to report one or more network metrics local to the respective reporting node. In response to the trigger message, a report of the one or more network metrics is received at the monitoring node from one of the one or more reporting nodes.
Abstract:
In one embodiment, a device determines a topological profile of individual nodes in a shared-media communication network, and also determines a respective likelihood of the nodes in the network to become a root of a floating topology based on the topological profiles. Accordingly, the device may provide instructions to particular nodes in the network based on the respective likelihoods.
Abstract:
In one embodiment, a device (e.g., learning machine) determines a plurality of fate-sharing group (FSG) nodes in a computer network that are prone to simultaneously send an alarm upon detecting an event. As such, the device may elect one or more FSG owner nodes as a subset of the FSG nodes, and instructs the FSG group such that only FSG owner nodes send an alarm upon event detection.
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:
Statistical and historical values of performance metrics are actively used to influence routing decisions for optimum topologies in a constrained network. Traffic service level is constantly monitored and compared with a service level agreement. If deviation exists between the monitored traffic service level and the terms of the service level agreement, stability metrics are used to maintain paths through the network that meet the terms of the traffic service level agreement or that improve the traffic flow through the network. Backup parent selection for a node in the network is performed based on previous performance of backup parents for the 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 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, techniques are shown and described relating to dynamically determining node locations to apply learning machine based network performance improvement. In particular, a degree of significance of nodes in a network, respectively, is calculated based on one or more significance factors. One or more significant nodes are then determined based on the calculated degree of significance. Additionally, a nodal region in the network of deteriorated network health is determined, and the nodal region of deteriorated network health is correlated with a significant node of the one or more significant nodes.
Abstract:
In one embodiment, techniques are shown and described relating to a distributed architecture for layered Hidden Markov Models. In particular, in one embodiment, a Hidden Markov Model (HMM) at a layer i receives a sequence of hidden state produced by an HMM at a layer i−1, and uses the sequence of hidden state produced by the HMM at layer i−1 as input to the HMM at layer i, where the HMM at layer i−1 uses first time period bins, and the HMM at layer i uses second time period bins that are greater in length than the first time period bins. In another embodiment, the HMM at layer i originates the input (e.g., from measured properties), and produces the sequence of hidden state to output it to an HMM at a layer i+1 for use as its input.