Abstract: Techniques are described for monitoring network performance and managing network faults in a computer network. A cloud-based network management system stores path data received from a plurality of network devices operating as network gateways for an enterprise network, the path data collected by each network device of the plurality of network devices for one or more logical paths of a physical interface from the network device over a wide area network (WAN). The network management system determines, based on the path data, one or more WAN link health assessments, wherein the one or more WAN link health assessments include a success or failure state associated with one or more of service provider reachability, physical interface operation, or logical path performance; and in response to determining the at least one failure state, outputs a notification including identification of a root cause of the at least one failure state.

Abstract: A network management system (NMS) generates a hierarchical attribution graph representing different scopes at different hierarchical levels of a wide area network (WAN); obtains logical path down data indicative of operational behavior including failure events associated with logical paths of network devices over the WAN; obtains total path data indicative of a historical number of active logical paths between the network devices; and determines a scope of a logical path down issue by, for a time period of the logical path down issue, determining a score for each scope of the different scopes based on the logical path down data aggregated across the respective scope and the total path data, and determining the scope of the logical path down issue as a particular scope of the different scopes having a highest score. The NMS may identify the particular scope as a root cause of the logical path down issue.

Abstract: Techniques are disclosed for a network management system (NMS) that performs quality of service (QoS) monitoring and troubleshooting of user experience issues occurring outside of a network managed by the NMS using data obtained from third-party sources. For example, an NMS obtains third-party data of a third-party application server or third-party service provider server from a third-party monitoring vendor. The NMS identifies a user experience issue indicated by the third-party data and stitches the third-party data to network data received from network devices. The NMS determines a root cause or a remedial action of the user experience issue based at least on the network data received from the one or more network devices. The NMS generates a notification for presentation to an administrator device which identifies the root cause or the remedial action of the user experience issue.

Abstract: Disclosed is a network management system that provides an interface to enable diagnostics and troubleshoot of a remotely managed multi-site network. Some embodiments provide a natural language interface, while other embodiments provide a chatbot type interface that communicates with a technician via traditional text information on a display screen. The diagnostic and troubleshooting capabilities search a central data store that receives device property information from each site of the multi-site network. Based on devices or users that match portions of the entity, queries to the data store are initiated to obtain additional data on the devices. A response to the query is then provided based on the properties of the devices.

Abstract: Various aspects of the subject technology relate to systems, methods, and machine-readable media for a learning platform that employs machine learning to identify patterns that describe meaningful subgroups of patients associated with a particular disease, distinct characteristics of each subgroup, and implications of belonging to a specific subgroup in terms of developing into a more severe disease state for patient journey mapping. The system may include a mapping server that includes a medical-event embedding engine that embeds electronic health records into vectors, a clustering engine that identifies clusters of patients with similar patient journeys by operating on the output of the medical-event embedding engine, and a cluster profiling engine that identifies distinguishing medical events for each identified cluster by operating on the output of the clustering engine.

Abstract: Disclosed is a network management system that provides an interface to enable diagnostics and troubleshoot of a remotely managed multi-site network. Some embodiments provide a natural language interface, while other embodiments provide a chatbot type interface that communicates with a technician via traditional text information on a display screen. The diagnostic and troubleshooting capabilities search a central data store that receives device property information from each site of the multi-site network. Based on devices or users that match portions of the entity, queries to the data store are initiated to obtain additional data on the devices. A response to the query is then provided based on the properties of the devices.

Abstract: An example system is configured to: identify, using network data for a plurality of network devices, a first network device of the plurality of network devices with which a network connection has a connectivity issue; identify, based on a network representation generated from the network data, one or more second network devices from the plurality of network devices that are connected to the first network device; identify a root cause of the connectivity issue based on analysis of the network connection with the first network device using the network data; and send, based on the identified root cause of the connectivity issue, to at least one of the one or more second network devices, instructions for the first network device to perform an action to remediate the connectivity issue, wherein the at least one of the one or more second network devices communicates the instructions to the first network device.

Abstract: Disclosed are embodiments for automatically resolving faults in a complex network system. Some embodiments monitor one or more of system operational parameter values and message exchanges between network components. A machine learning model detects a fault in the complex network system, and an action is selected based on a cause of the fault. After the action is applied to the complex network system, additional monitoring is performed to either determine the fault has been resolved or additional actions are to be applied to further resolve the fault.

Abstract: This disclosure describes a network management system (NMS) configured to determine a particular network device of a plurality of network devices based on a first user input in a conversational assistant. The one or more processors are further configured to identify a set of actionable insights for the particular network device based on network data received from the plurality of network devices and determine a set of views of a dashboard based at least on the set of actionable insights, wherein each view of the set of views displays a portion of the network data received from the plurality of network devices. The one or more processors are further configured to select a view of the set of views of the dashboard based on a second user input in the conversational assistant and cause the dashboard to display the selected view.

Abstract: A network management system (NMS) is described that includes one or more processors coupled to memory storing network data. The one or more processors are configured to receive a query identifying a site and determine, based on the network data, a first set of troubleshooting issues for a wide area network (WAN) deployment at the site, a second set of troubleshooting issues for a wireless deployment at the site, and a third set of troubleshooting issues for a wired deployment at the site. The one or more processors are configured to determine, based on a user experience metric, a first troubleshooting issue from the first set of troubleshooting issues for the WAN deployment, a second troubleshooting issue from the second set of troubleshooting issues for the wireless deployment, and a third troubleshooting issue from the third set of troubleshooting issues for the wired deployment.

Abstract: Techniques are disclosed for a network management system (NMS) that performs quality of service (QOS) monitoring and troubleshooting of user experience issues occurring outside of a network managed by the NMS using data obtained from third-party sources. For example, an NMS obtains third-party data of a third-party application server or third-party service provider server from a third-party monitoring vendor. The NMS identifies a user experience issue indicated by the third-party data and stitches the third-party data to network data received from network devices. The NMS determines a root cause or a remedial action of the user experience issue based at least on the network data received from the one or more network devices. The NMS generates a notification for presentation to an administrator device which identifies the root cause or the remedial action of the user experience issue.

Abstract: The disclosure describes one or more techniques for performing automated scheduling and/or orchestration of synthetic tests for network sites. For example, a network management system comprises a memory and one or more processors in communication with the memory and configured to: determine, based on data received from a plurality of network devices of a network, a network condition to perform a test, wherein the network condition comprises one or more of a time window to perform the test or one or more network devices of the plurality of network devices to perform the test; instruct, based on the network condition, the one or more network devices to perform the test; identify, based on data obtained from the one or more network devices that performed the test, an issue of the network; and perform an action based on the identified issue.

Abstract: Techniques are described for detecting that a client device physically connected to a network device is “stuck,” that is, the client device is not sending or receiving network packets with the network device. A network management system (NMS) receives current network statistics of ports of network devices with respect to client devices physically connected to the ports. The NMS identifies a candidate client device connected to a particular port of a particular network device for which the current network statistics indicate an issue. The NMS detects anomalous behavior of the candidate client device based on one or more features of the current network statistics, historical baseline statistics associated with the candidate client device, and peer statistics associated with one or more peer client devices of a same device type as the candidate client device. The NMS outputs a notification of the anomalous behavior.

Abstract: An example system includes network devices at a site; and a network management system (NMS) that is configured to: identify a first network device of the plurality of network devices with which a network connection has been lost; identify, based on a network topology graph generated from the network data, one or more neighbor network devices of the plurality of network devices that are connected to the first network device; perform root cause analysis of the lost connection with the first network device based on the network data to identify a root cause of the lost connection; and send, to a neighbor network device selected from the one or more neighbor network devices and based on the identified root cause, instructions for the first network device to perform an action to remediate the lost connection, wherein the neighbor network device communicates the instructions to the first network device.

Abstract: An example network analysis system includes a memory storing telemetry data received from a plurality of network devices, the plurality of network devices includes extract entity information and connectivity information from the received telemetry data, wherein the entity information represents one or more network devices of the plurality of network devices and the connectivity information represents network connections between one or more devices of the plurality of network devices; and store the connectivity information and entity information as a network topology graph in a graph database, wherein the entity information is stored as nodes of the network topology graph and the connectivity information is stored as edges of network topology graph, and wherein the network topology graph represents an organization level topology of the organization network.

Abstract: Disclosed are embodiments for automatically resolving faults in a complex network system. Some embodiments monitor one or more of system operational parameter values and message exchanges between network components. A machine learning model detects a fault in the complex network system, and an action is selected based on a cause of the fault. After the action is applied to the complex network system, additional monitoring is performed to either determine the fault has been resolved or additional actions are to be applied to further resolve the fault.

Abstract: A network management system (NMS) is described that provides a granular troubleshooting workflow at an application session level using an application session-specific topology from a client device to a cloud-based application server. During an application session of a cloud-based application, a client device running the application exchanges data through one or more access point (AP) devices, one or more switches at a wired network edge, and one or more network nodes, e.g., switches, routers, and/or gateway devices, to reach a cloud-based application server. For a particular application session, the NMS generates a topology based on network data received from a subset of network devices, e.g., client devices, AP devices, switches, routers, and/or gateways, that were involved in the particular application session over a duration of the particular application session. In this way, the NMS enables backward-looking troubleshooting of the particular application session.

Abstract: A network management system (NMS) is described that includes one or more processors coupled to memory storing network data. The one or more processors are configured to receive a query identifying a site and determine, based on the network data, a first set of troubleshooting issues for a wide area network (WAN) deployment at the site, a second set of troubleshooting issues for a wireless deployment at the site, and a third set of troubleshooting issues for a wired deployment at the site. The one or more processors are configured to determine, based on a user experience metric, a first troubleshooting issue from the first set of troubleshooting issues for the WAN deployment, a second troubleshooting issue from the second set of troubleshooting issues for the wireless deployment, and a third troubleshooting issue from the third set of troubleshooting issues for the wired deployment.

Abstract: A network management system (NMS) is described that provides a granular troubleshooting workflow at an application session level using an application session-specific topology from a client device to a cloud-based application server. During an application session of a cloud-based application, a client device running the application exchanges data through one or more access point (AP) devices, one or more switches at a wired network edge, and one or more network nodes, e.g., switches, routers, and/or gateway devices, to reach a cloud-based application server. For a particular application session, the NMS generates a topology based on network data received from a subset of network devices, e.g., client devices, AP devices, switches, routers, and/or gateways, that were involved in the particular application session over a duration of the particular application session. In this way, the NMS enables backward-looking troubleshooting of the particular application session.