This disclosure pertains to computer-assisted methods, apparatus, and systems for efficiently monitoring and storing the state of large-scale communication networks and tracing packet routes through such networks.
This disclosure pertains to computer-assisted methods, apparatus, and systems for forecasting network performance responsive to network reconfigurations in large scale communication networks.
This disclosure pertains to computer-assisted methods, apparatus, and systems for efficiently monitoring and storing the state of large-scale communication networks and tracing packet routes through such networks.
This disclosure pertains to computer-assisted methods, apparatus, and systems for efficiently monitoring and storing the state of large-scale communication networks and tracing packet routes through such networks.
This disclosure pertains to computer assisted methods, apparatus, and systems for determining a path that a data packet would have traversed through a communication network at a particular point in time, which may be used, for instance, to determine the sources of connectivity failures in a large scale communication network.
H04L 41/12 - Discovery or management of network topologies
H04L 41/082 - Configuration setting characterised by the conditions triggering a change of settings the condition being updates or upgrades of network functionality
H04L 43/067 - Generation of reports using time frame reporting
Systems, apparatuses, and methods for root cause analysis of a computing network are disclosed. A network management system builds a causation model based on causal mappings corresponding to network events. The causal mapping identifies a logical order of occurrence between a given network event and other network events. Network event obtained from the computing network is analyzed using the causation model for performing a root cause analysis for the computing network by generating a network event graph defining a one-to-one relationship between a given network event and one or more other network events. The causation model is built using determined hierarchical relationship of network events with a plurality of network entities as connected within the computing network.
H04L 41/069 - Management of faults, events, alarms or notifications using logs of notificationsPost-processing of notifications
H04L 41/0631 - Management of faults, events, alarms or notifications using root cause analysisManagement of faults, events, alarms or notifications using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
Systems, apparatuses, and methods for root cause analysis of a computing network are disclosed. A network management system builds a causation model based on causal mappings corresponding to network events. The causal mapping identifies a logical order of occurrence between a given network event and other network events. Network event obtained from the computing network is analyzed using the causation model for performing a root cause analysis for the computing network by generating a network event graph defining a one-to-one relationship between a given network event and one or more other network events. The causation model is built using determined hierarchical relationship of network events with a plurality of network entities as connected within the computing network.
H04L 41/0631 - Management of faults, events, alarms or notifications using root cause analysisManagement of faults, events, alarms or notifications using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
H04L 43/106 - Active monitoring, e.g. heartbeat, ping or trace-route using time related information in packets, e.g. by adding timestamps
8.
METHODS AND APPARATUS FOR NETWORK TRACING, FORECASTING, AND CAPACITY PLANNING
This disclosure pertains to computer-assisted methods, apparatus, and systems for forecasting network performance responsive to network reconfigurations in large scale communication networks.
Systems, apparatuses, and methods for generating training data for machine learning models are disclosed. In an implementation training data for machine learning models can be created without requiring manual labeling and annotation of data. Natural language data from dashboards and widgets is extracted to identify user context. The user context is used to generate alias data strings that each correlate user context with natural language text. These alias data strings are used to train the machine learning model. Using user context derived from natural language text as basis to train the models, allows the system to generate accurate training data, without needing an end-user or system administrator to create a labeled set of data.
This disclosure pertains to computer-assisted methods, apparatus, and systems for forecasting network performance responsive to network reconfigurations in large scale communication networks.
Systems, apparatuses, and methods for maintenance aware performance reporting are disclosed. Responsive to a request for a performance report of devices and/or services for a given time range, reportable data is computed. Checks are performed to identify any maintenance windows that need to be configured in that time range. If such windows are to be configured, the performance insights for the entity are modified, without requiring to perform a reanalysis of their entire performance data. This is performed by utilizing metadata defining the performance insights, that are maintained along with performance stored in individual time-based datasets. Based on these modifications, updated performance reports can be created on-the-fly accounting for previously scheduled but unreported events. Further, the update is performed without reanalysis of original data, thereby maintaining efficiency and accuracy of the reports.
Systems, apparatuses, and methods for maintenance aware performance reporting are disclosed. Responsive to a request for a performance report of devices and/or services for a given time range, reportable data is computed. Checks are performed to identify any maintenance windows that need to be configured in that time range. If such windows are to be configured, the performance insights for the entity are modified, without requiring to perform a reanalysis of their entire performance data. This is performed by utilizing metadata defining the performance insights, that are maintained along with performance stored in individual time-based datasets. Based on these modifications, updated performance reports can be created on-the-fly accounting for previously scheduled but unreported events. Further, the update is performed without reanalysis of original data, thereby maintaining efficiency and accuracy of the reports.
Systems, apparatuses, and methods for generating training data for machine learning models are disclosed. In an implementation training data for machine learning models can be created without requiring manual labeling and annotation of data. Natural language data from dashboards and widgets is extracted to identify user context. The user context is used to generate alias data strings that each correlate user context with natural language text. These alias data strings are used to train the machine learning model. Using user context derived from natural language text as basis to train the models, allows the system to generate accurate training data, without needing an end-user or system administrator to create a labeled set of data.
This disclosure pertains to computer-assisted methods, apparatus, and systems for tracing the sources of connectivity failures in large scale communication networks.
This disclosure pertains to computer assisted methods, apparatus, and systems for determining a path that a data packet would have traversed through a communication network at a particular point in time, which may be used, for instance, to determine the sources of connectivity failures in a large scale communication network.
H04L 41/12 - Discovery or management of network topologies
H04L 41/082 - Configuration setting characterised by the conditions triggering a change of settings the condition being updates or upgrades of network functionality
H04L 43/067 - Generation of reports using time frame reporting
Systems, apparatuses, and methods for analyzing log data are described. A processing unit ingests data blocks multiple data sources associated with a network-connected device. Each data block is associated with contextual meta tags identified from the ingested data. Further, one or more entities associated with the network-connected device are identified and for each entity a taxonomy is created. The taxonomy comprises a plurality of categories, each category comprising at least one contextual meta tag. Dashboards for presentation of processed log data are generated based at least in part on the taxonomy.
Systems, apparatuses, and methods for analyzing log data are described. A processing unit ingests data blocks multiple data sources associated with a network-connected device. Each data block is associated with contextual meta tags identified from the ingested data. Further, one or more entities associated with the network-connected device are identified and for each entity a taxonomy is created. The taxonomy comprises a plurality of categories, each category comprising at least one contextual meta tag. Dashboards for presentation of processed log data are generated based at least in part on the taxonomy.
Systems, apparatuses, and methods are disclosed to identify sustained and significant changes in transceiver metrics for predicting transceiver failure. A percentage of change with respect to a previous value in the metric can be tracked over a configurable period of time. Anomalies can be identified if such change persists over a minimum configurable period of time. The metrics as well as transceiver metadata are correlated to perform an outlier detection. Optical transceivers of the same type (characterized by a combination of vendor, type, and model) are analyzed for outlier detection. A variability factor is taken into account for metrics, as an indicator of a likely hardware condition that may be precursor of a failure.
Systems, apparatuses, and methods are disclosed to identify sustained and significant changes in transceiver metrics for predicting transceiver failure. A percentage of change with respect to a previous value in the metric can be tracked over a configurable period of time. Anomalies can be identified if such change persists over a minimum configurable period of time. The metrics as well as transceiver metadata are correlated to perform an outlier detection. Optical transceivers of the same type (characterized by a combination of vendor, type, and model) are analyzed for outlier detection. A variability factor is taken into account for metrics, as an indicator of a likely hardware condition that may be precursor of a failure.
H04B 10/077 - Arrangements for monitoring or testing transmission systemsArrangements for fault measurement of transmission systems using an in-service signal using a supervisory or additional signal
H04B 10/079 - Arrangements for monitoring or testing transmission systemsArrangements for fault measurement of transmission systems using an in-service signal using measurements of the data signal
H04L 41/00 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
20.
QUERY CHAIN - DECLARATIVE APPROACH FOR ON-DEMAND DASHBOARDING
Systems, apparatuses, and methods for creating on-demand dashboards for inspecting performance metrics of hardware and/or software infrastructure are described. A request is received, from a user device, to inspect one or more performance metrics of a network connected service. An operations management system generates metadata from data ingested from one or more data sources associated with the network connected service. A plurality of database queries generated based on the contextual metadata are identified and at least a portion of the plurality of database queries are resolved. One or more dashboards facilitating inspection of each of the one or more performance metrics are generated based at least in part on the resolved portion of the plurality of database queries.
Systems, apparatuses, and methods for creating on-demand dashboards for inspecting performance metrics of hardware and/or software infrastructure are described. A request is received, from a user device, to inspect one or more performance metrics of a network connected service. An operations management system generates metadata from data ingested from one or more data sources associated with the network connected service. A plurality of database queries generated based on the contextual metadata are identified and at least a portion of the plurality of database queries are resolved. One or more dashboards facilitating inspection of each of the one or more performance metrics are generated based at least in part on the resolved portion of the plurality of database queries.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
(1) Computer analytics software for managing multi-domain computer networks and cloud infrastructures that identifies application performance issues, detects network anomalies, and isolates digital infrastructure security vulnerabilities. (1) Software as a service (SAAS) service, namely software for managing multi-domain computer networks and cloud infrastructures that identifies application performance issues, detects network anomalies, and isolates digital infrastructure security vulnerabilities.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
(1) Computer analytics software for managing multi-domain computer networks and cloud infrastructures that identifies application performance issues, detects network anomalies, and isolates digital infrastructure security vulnerabilities. (1) Software as a service (SAAS) service, namely software for managing multi-domain computer networks and cloud infrastructures that identifies application performance issues, detects network anomalies, and isolates digital infrastructure security vulnerabilities.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer analytics software for managing multi-domain
computer networks and application infrastructures that
identifies application performance issues, detects network
anomalies, and isolates digital infrastructure problems. Software as a service (SAAS) for managing multi-domain
computer networks and application infrastructures that
identifies application performance issues, detects network
anomalies, and isolates digital infrastructure problems.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer analytics software for managing multi-domain
computer networks and application infrastructures that
identifies application performance issues, detects network
anomalies, and isolates digital infrastructure problems. Software as a service (SAAS) for managing multi-domain
computer networks and application infrastructures that
identifies application performance issues, detects network
anomalies, and isolates digital infrastructure problems.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable computer analytics software for managing multi-domain computer networks and application infrastructures that identifies application performance issues, detects network anomalies, and isolates digital infrastructure problems Software as a service (SAAS) services featuring software for managing multi-domain computer networks and application infrastructures that identifies application performance issues, detects network anomalies, and isolates digital infrastructure problems
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Downloadable computer analytics software for managing multi-domain computer networks and application infrastructures that identifies application performance issues, detects network anomalies, and isolates digital infrastructure problems Software as a service (SAAS) services featuring software for managing multi-domain computer networks and application infrastructures that identifies application performance issues, detects network anomalies, and isolates digital infrastructure problems
