A lazy graph construction with compression includes receiving a query related to a network; one of generating a site graph for the network and accessing the site graph already in memory; performing a search on the site graph based on the query; accessing data in a database and generating, in the memory, a plurality of sub-graphs using the data, for the query; and providing a solution to the query based on the search and a search of the plurality of sub-graphs. The site graph can be at a site level and includes network elements and associated connections in the network, and the plurality of sub-graphs can be at an intra-site level including any of component-to-component connections and intra-component connections.

A method for managing networking devices. The method includes receiving, by a second management system, first network device state information (NDSI) for a first plurality of network devices, where the first NDSI is obtained by a first management system and where the first management system manages the first plurality of network devices. The method further includes initiating performance of a management action based on a result, wherein the result is obtained by processing at least the first NDSI.

Embodiments are generally directed apparatuses, methods, techniques and so forth determine an access level of operation based on an indication received via one or more network links from a pod management controller, and enable or disable a firmware update capability for a firmware device based on the access level of operation, the firmware update capability to change firmware for the firmware device. Embodiments may also include determining one or more configuration settings of a plurality of configuration settings to enable for configuration based on the access level of operation, and enable configuration of the one or more configuration settings.

The disclosed technology teaches automating evaluation of QoE for data communication services in a wireless network—accessing performance indicators, mappings for the performance indicators to SKQIs in a SKQI parameters table, and mappings for SKQIs to KQIs in an SKQI-to-KQI mapping table. The disclosed method includes configuring first and second functions that, respectively, roll up performance indicators into SKQIs based on parameters stored in the SKQI parameters table to calculate SKQI scores and weights, and roll up SKQIs into KQIs and QoE based on parameters stored in SKQI-to-KQI and SKQI-to-QoE mapping tables, to calculate KQI and QoE scores. Also disclosed is generating first SQL statements that invoke the first function to calculate the SKQIs and second SQL statements that invoke the second function to calculate KQIs and QoE, storing generated first and second SQL statements in non-volatile storage and calculating KQIs and QoE using the automatically generated SQL statements.

A discovery application on a computing system is provided. The discovery application receives a user input, which is for discovery of resources associated with a cloud operating system of a cloud computing system. The user input includes an authentication credential and account information associated with the cloud operating system. Based on the received input, the discovery application executes a discovery pattern comprising operations for the discovery of resources. The cloud operating system includes a group of services to access such resources. At least one of the operations corresponds to an API call to an API endpoint associated with a service of the group of services. The discovery application receives a response to the API call from the cloud operating system. The response includes a payload of information associated with the resources. The discovery application updates, based on the received response, one or more configuration items in a configuration management database.

Illustrative embodiments provide an efficient, resilient high-availability (“HA”) router architecture. In illustrative embodiments, metric and event data is made resilient to node failover by replication. The same data is stored on the database of each router. Instead of pushing metrics directly from all of each node's software to each node's database, replication is instead handled by the database loader application subscribing to queues on both nodes. In some embodiments, records are written in the queues to topics that have a certain time to live (TTL), so the loader on one node has the duration of the TTL to read from the other node's queue in order to achieve replication.

A user experience workflow may be configured based on input received for various object types selectively arranged within the user experience workflow and then bound to a destination identifier, such as a telephone number or web address. A user interface of software for configuring a user experience workflow is presented at a user device and input from that user device is used to selectively arrange objects within a user experience workflow and/or to configure objects thereof. After configurations are applied to the objects, the user experience workflow is bound to the destination identifier. An end user device which accesses the destination identifier (e.g., by calling the telephone number, visiting the web address, or using an application connecting to the web address) may then traverse the user experience workflow, including in some cases having configured content presented thereto.

A novel machine learning-based network analytics, troubleshoot, and self-healing system identifies and locates sources of data network problems autonomously within an entire end-to-end network topology of a network operator, while not necessitating human diagnosis of the data network problems. This system uniquely embeds a smart universal telemetry (SUT) as a quality-of-experience (QoE) parameter collection agent in intermediary transport-level network equipment and each end-user modem, which in turn enables on-demand collection of robust diagnostic data from all end-user modems and intermediary transport level nodes in a data network. By executing a machine learning (ML)-based artificial intelligence (AI) analytical module in a cloud-computing resource, the system then achieves autonomous identification and source pinpointing of network problems, and even self-repairs some machine-identified data network problems autonomously through remote software updates performed intelligently by the ML-based AI analytical module, if physical replacement of a network equipment is unnecessary to resolve such problems.

One or more non-transitory computer readable media contain program instructions that, when executed by one or more processors, cause the one or more processors to perform operations including: creating and managing a plurality of smart entities, each of the smart entities including a plurality of attributes; receiving inbound data from one or more enterprise applications; translating the inbound data into values for one or more of the plurality of attributes; writing the plurality of attributes to the smart entities; reading the plurality of attributes from the smart entities; translating the plurality of attributes into outbound data; and providing the outbound data to the one or more enterprise applications.

A nodal communication network of a process control or factory automation system includes nodes that are highly versatile (HV) field devices storing respective tag identifications. A DHCP server assigns respective endpoint identifications to nodes that connect to the network. A network node manager manages a mapping database that stores associations of tag identifications of network nodes with the assigned endpoint identifications, and a state database that stores updated states of the network nodes. A DNS server responds, in conjunction with the network node manager, to requests for endpoint identifications to allow nodes to be discovered within the network. An HV device node is a data source that establishes and maintains (sometimes simultaneously) respective communication sessions over the network with one or more other nodes that are consumers of the data generated by the HV devices, and data delivered via the communication sessions may be used to control an industrial process.