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: 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: An example method includes receiving, from a network device, data indicating characterizations of network traffic on a plurality of ports of the network device; determining, by processing circuitry, for each port of the plurality of ports, an indicator of a port type for the port based on the data indicating the characterizations of network traffic on the plurality of ports, wherein the port type indicates a link type of network traffic exchanged by the port; and outputting, by the processing circuitry, the indicator of the port type to an output device.

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: 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: 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: A network management system (NMS) comprises one or more processors coupled to memory. The one or more processors of are configured to determine a list of network devices from the plurality of network devices based on an entity type and determine a suggested filter attribute based on the list of network devices and one or more of a user profile, a current state of the plurality of network devices, or a current state of the networking services. The one or more processors are further configured to output, in a user interface, an indication of the suggested filter attribute and, in response to receiving user input representative of a selection of the indication of the suggested filter attribute, determine a filtered list of network devices from the list of network devices using the suggested filter attribute and output, in the user interface, an indication of the filtered list of network devices.

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: 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: 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: 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: 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: 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: A method for controlling a ratio of multi-CNAME traffic, includes: receiving, by a target authoritative DNS server, an A record query request for a target domain name sent by a local DNS server, and determining, by the target authoritative DNS server, all CNAME domain names according to stored CNAME records of the target domain name; acquiring, by the target authoritative DNS server, IP addresses corresponding to each CNAME domain name of all the CNAME domain names from an authoritative DNS server corresponding to each CNAME domain name; combining, by the target authoritative DNS server, IP addresses corresponding to all the CNAME domain names according to a preset traffic pulling ratio of the target domain name, to generate at least one IP group; and randomly feeding back, by the target authoritative DNS server, one IP group of the at least one IP group to the local DNS server.

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: 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: The present disclosure provides an DNS server and configuration loading method, an DNS network system, and a domain name resolution method and system. The present disclosure relates to the field of network communication technologies. The DNS server configuration loading method includes: dividing at least two NS partitions according to network carriers and/or regions; based on an IP address of an DNS server, obtaining an NS partition on which the DNS server has a traffic; from local records or database, filtering out domain name configuration information belonging to the NS partition corresponding to the DNS server; and loading the filtered-out domain name configuration information into a memory of the DNS server. The present disclosure significantly reduces the amount of DNS configuration; increases the efficiency for the DNS server to load the domain name configuration information; reduces the loading time; and reduces the memory resource consumption of the DNS server.

Abstract: The present disclosure provides a DNS network system, and a domain-name parsing method and system. A local DNS server receives a domain-name parsing request from a client terminal a network operator in the same network and sends the domain-name parsing request to a root server; based on an NS record of an upper-level authoritative DNS server returned by the root server, sends the domain-name parsing request to the upper-level authoritative DNS server; based on an NS record of an external authorized server returned by the upper-level authoritative DNS server, sends the domain-name parsing request to the external authorized server; based on an A-record of a lower-level authoritative DNS server returned by the external authorized server, sends the domain-name parsing request to the lower-level authoritative DNS server and receives a domain-name parsing result sent by the lower-level authoritative DNS server.