Abstract: A system monitors a network or web application provided by one or more distributed applications and provides data for each and every method instance in an efficient low-cost manner. Agents may monitor the performance of the distributed application by the web services and report monitoring data as runtime data to the remote server, for example a controller. The controller may analyze the data to identify one or more performance issues or “hot spot” methods based on current or past performance, functionality, content, or business relevancy. Instructions and/or configuration information may be transmitted by the controller to the agents that correspond to a particular business transaction portion associated with a hot spot. The portions are then monitored to collect data associated with the hot spot and the hot spot data is reported back to the controller.

Abstract: In one embodiment, a network assurance service that monitors a network receives a plurality of key performance indicators (KPIs) for a networking device in the network over time. The network assurance service represents relationship changes between the KPIs over time as a set of one or more KPI trajectories. The network assurance service uses a machine learning-based model to determine that a behavior of the networking device is anomalous, based on the one or more KPI trajectories. The network assurance service provides an indication of the anomalous behavior of the networking device to a user interface.

Abstract: A system may be configured to identify, from among a plurality of access points in an area in communication with a network, a plurality of edge access points associated with an edge of the area. In addition, the system may also be configured to determine first edge location-related data for a first edge access point and first interior location-related data for a first interior access point. Determining the first interior location-related data for the first interior access point includes exchanging ranging data indicative of a first relative distance between the first edge access point and the first interior access point. The ranging data may be based at least in part on fine timing measurements and/or time-of-flight-based measurements. Determining the first interior location-related data for the first interior access point may also include communicating the first edge location-related data from the first edge access point to the first interior access point.

Abstract: Techniques and mechanisms for detecting and deducing of urgent messages in low-power and lossy networks (LLNs) using a correlation analysis of the nodes within a network and machine learning (ML) models. Utilizing these techniques, a field network director (FND) of the network can determine neighboring devices within the network. ML models may be utilized to determine that based upon receipt of a power outage notification (PON) message and/or a power restoration notification (PRN) message from nodes, neighboring nodes of the nodes may also have suffered a power outage and/or a subsequent power restoration, even if the FND did not receive a corresponding PON message and/or a corresponding PRN message from the neighboring nodes of the network. Thus, loss of power and subsequent power restoration may be handled for large numbers of neighboring nodes within the network, even when only a few PON messages and/or subsequent PRN messages are received.

Abstract: In one illustrative example, a network node (e.g. a router or switch) may receive a data packet and timestamp a copy of the data packet. The node may also compute a signature for the copy and insert the signature in a header of the copy. The node may send the copy to a controller for correlation with one or more other timestamped data packet copies of the data packet from one or more other network nodes having the same signature and for the computation of delay. The original data packet may be forwarded to a next network node without any timestamp or other metadata added to it. The processing of the data packets may be performed as part of a function for punting the timestamped data packet copy and forwarding, or as a function for forwarding and punting the timestamped data packet copy.

Abstract: In one embodiment, a device in a network receives in-situ operations administration and management (iOAM) data regarding a plurality of traffic flows in the network. The iOAM data comprises entropy values for the plurality of traffic flows. The device receives network topology information indicative of network paths available in the network. The device generates a machine learning-based entropy topology model for the network based on the received iOAM data and the received network topology information. The entropy topology model maps path selection predictions for the network paths with entropy values. The device uses the entropy topology model to cause a particular traffic flow to use a particular network path.

Abstract: In one embodiment, a device in a network receives traffic data regarding a plurality of observed traffic flows. The device maps one or more characteristics of the observed traffic flows from the traffic data to traffic characteristics associated with a targeted deployment environment. The device generates synthetic traffic data based on the mapped traffic characteristics associated with the targeted deployment environment. The device trains a machine learning-based traffic classifier using the synthetic traffic data.

Abstract: In an embodiment, a computer-implemented method comprises receiving logical model input that specifies a logical topology model of networking elements and/or computing elements for deployment at least partially in a private cloud computing infrastructure and at least partially in a public cloud computing infrastructure; receiving resource input specifying an inventory of computing elements that are available at least partially in the private cloud computing infrastructure and at least partially in the public cloud computing infrastructure; automatically generating an intermediate topology comprising a set of deployment instructions that are capable of execution at least partially in the private cloud computing infrastructure and at least partially in the public cloud computing infrastructure to cause physical realization of a network deployment corresponding to the logical topology model; determining whether the intermediate topology is functionally equivalent to the logical topology model; in response to det

Abstract: In one embodiment, a device in a network receives, at its wireless receiver, a preamble of a spatially modulated packet. The device analyzes the preamble of the packet to identify a transmit antenna index of the packet. The device determines that the packet was not destined for the device, based on the transmit antenna index of the packet. The device depowers, prior to decoding the complete packet, the wireless receiver of the device, based on the determination that the packet was not destined for the device.

Abstract: In one embodiment, a network analysis process initiates network path analysis for a transaction application operating over a logical transaction path having a first segment from a first set of transaction servers to a load balancer and a second segment then to a second set of transaction servers. The network path analysis, when for the second segment, comprises: selecting a receiving transaction server of the second set of transaction servers; identifying a TCP session associated with the transaction application already in progress to the receiving transaction server; initiating a TCP traceroute using ACK packets, whose signature matches the in-progress TCP session, from the receiving transaction server to the load balancer; and determining, in reverse, a network path of layer-3 segments and associated network metrics between the receiving transaction server and the load balancer.

Abstract: In one embodiment, a service chain data packet is instrumented as it is communicated among network nodes in a network providing service-level and/or networking operations visibility. The service chain data packet includes a particular header identifying a service group defining one or more service functions, and is a data packet and not a probe packet. A network node adds networking and/or service-layer operations data to the particular service chain data packet, such as, but not limited to, in the particular header. Such networking operations data includes a performance metric or attribute related to the transport of the particular service chain packet in the network. Such service-layer operations data includes a performance metric or attribute related to the service-level processing of the particular service chain data packet in the network.

Abstract: In one embodiment, a device in a mesh network rooted at a root node receives a subroot selection notification. The subroot selection notification indicates that the device should function as a root of a sub-directed acyclic graph (DAG) were a power outage to occur. The device determines that a power outage has occurred. The device forms, after determining that a power outage has occurred, a sub-DAG that is rooted at the device by establishing one or more other devices in the mesh network as routing children of the device in the sub-DAG. The device joins the sub-DAG to a DAG rooted at the root node.

Abstract: In one embodiment, a device applies clustering to traffic characteristics of application traffic in a software-defined wide area network (SD-WAN) associated with a particular application, to form a cluster of traffic characteristics. The device selects a tunnel in the SD-WAN to probe. The device generates, based on the cluster, packets that mimic the application traffic. The device probes the selected tunnel by sending the generated packets via the tunnel.

Abstract: A source virtual private network (VPN) gateway supports a local source subnet and communicates over a wide area network (WAN) with a destination VPN gateway that supports a local destination subnet. The source VPN gateway receives from the local source subnet an Internet Protocol (IP) packet destined for the local destination subnet, determines a security association (SA) based on a source IP address and a destination IP address of the IP packet, and encapsulates the IP packet with tunnel encapsulation including a tunnel protocol header and a tunnel outer IP header, to produce a clear-text tunnel packet. The source VPN gateway encrypts the IP packet and the tunnel protocol header but not the tunnel outer IP header using an encryption key and a security parameter index for the SA, to produce an encrypted tunnel packet, and tunnels it to the destination VPN gateway over the WAN.

Abstract: In one embodiment, a device in a wireless network receives telemetry data from a plurality of autonomous vehicles. The telemetry data is indicative of radio signal quality metrics experienced by the vehicles at a particular location over time. The device forms an array of wireless roaming thresholds by applying regression to the telemetry data. The device computes an optimum roaming threshold from the array of wireless roaming thresholds to be used by the vehicles when approaching the location. The device triggers, based on the computed optimum threshold, one or more of the autonomous vehicles to initiate access point roaming when approaching the particular location.

Abstract: A method, performed by a wireless resource controller, includes receiving an indication that a first wireless device is experiencing data loss over a communication channel provided by a wireless access point utilizing a first radio access technology (RAT). The method includes determining a plurality of congestion window values associated with other wireless devices that share a shared wireless resource with the first wireless device. The shared wireless resource is provided by a base station of a cellular network utilizing a second RAT. The method includes determining an initial congestion window value for the first wireless device based on the plurality of congestion window values associated with the other wireless devices. The method includes transmitting data to the first wireless device from the base station in accordance with the initial congestion window value for the first device in order to satisfy a performance criterion for the first wireless device.

Abstract: Chat-based interaction with an in-meeting virtual assistant may be provided. First, audio input associated with a meeting may be received. Next, an intent from the audio input may be detected. Text content associated with the audio input may then be generated in response to detecting the intent from the audio input. The text content may be displayed in a chat interface.

Abstract: Network interworking with no cross-domain state may be provided. First, an edge node may receive a packet from an intermediate node in a first domain. The edge node may be between the first domain and a second domain. Next, the edge node may pop, in response to a first Service Identifier (SID) in the packet, headers corresponding to the first domain from the packet. The edge node may then push, in response to the first SID, a label stack corresponding to the second domain onto the packet. The first SID may include data corresponding to the label stack. Then the edge node may route the packet to the second domain destine to an end node in the second domain.

Abstract: A method for providing enhanced visibility in heterogeneous network. The method comprises receiving, at a networking device housing a cellular base station and a WLAN access point, cellular network traffic from an electronic device. The method further comprises receiving, at the networking device, WLAN traffic from the electronic device and encapsulating, at the networking device, the cellular network traffic and the WLAN traffic using a common protocol. The method further comprises transmitting the encapsulated cellular traffic and the encapsulated WLAN traffic to a controller.

Abstract: In one embodiment, a device in a network intercepts a Domain Name System (DNS) query sent by a node in the network to a DNS service. The device inserts metadata information about the node into the DNS query before sending the DNS query on to the DNS service. The device extracts policy information regarding the node from a DNS response sent from the DNS service back to the node in response to the DNS query. The device implements a network policy for the node within the network based on the policy information extracted from the DNS response.