Techniques are described in which a network management system (NMS) is configured to determine a root cause of degraded network performance based on SLE metrics and the locations associated with network devices providing the SLE metrics. The NMS can determine service level experience (SLE) metrics associated with each client device on a network and location data for each client device of the plurality of client devices. The NMS can generate a time series of parameter vectors, where each parameter vector includes SLE metrics corresponding to each client device of the plurality of client devices. Each parameter vector is associated with the location of the client device corresponding to the SLE metrics. The NMS can determine, based on the time series of parameter vectors and associated locations, a root cause for a degradation in SLE metrics associated with the one or more of the client devices.

Methods and systems described herein are directed resolving object dependencies in a data center. A trie data structure that represents network paths of objects utilized by a selected source object is constructed. The trie data structure comprises nodes linked by edges. Each node represents an edge identification (“ID”) of source objects and destination objects of one or more network paths of objects utilized by the selected source object in a user-defined time interval. The trie data structure is traversed to resolve the different versions of source objects and destination objects utilized by the selected source object in subintervals of the time interval. A graph of the objects and destination objects utilized by the selected source object in the subintervals is generated and used to identify source objects and destination objects utilized by the selected source object during a performance problem of the selected source object.

In one embodiment, a device obtains user experience metrics for a plurality of sessions with an online application. The device detects a plurality of anomalies from among the user experience metrics. The device determines, based on a correlation between the plurality of anomalies, that a particular path entity is a root cause of the plurality of anomalies. The particular path entity comprises an egress service provider or data center of the online application. The device provides an indication of the particular path entity being the root cause of the plurality of anomalies.

In one embodiment, a device obtains user experience metrics for a plurality of sessions with an online application. The device detects a plurality of anomalies from among the user experience metrics. The device determines, based on a correlation between the plurality of anomalies, that a particular path entity is a root cause of the plurality of anomalies. The particular path entity comprises an egress service provider or data center of the online application. The device provides an indication of the particular path entity being the root cause of the plurality of anomalies.

In one embodiment, a device obtains telemetry data for network paths to a plurality of servers for an online application. The telemetry data includes application experience metrics based on feedback provided by users of the online application. The device decomposes the telemetry data for the network paths from different vantage points. The device also identifies, using the decomposed telemetry data, a particular endpoint of the online application as a cause of application experience degradation for the online application. The device provides an alert indicative of the particular endpoint of the online application being the cause of quality of experience degradation for the online application.

Embodiments of the present disclosure provide improved identification and handling of root causes for data event(s). Some embodiments improve the accuracy of determinations of a root cause or likely order of root causes of a data event affecting any number of system(s), and cause transmission of data associated with such root cause(s) for use in triaging such data event(s) and/or facilitating efficient servicing to resolve the data event. Some embodiments utilize modified centrality algorithm(s) to efficiently and accurately identify a likely root cause of a data event in a computing environment. Some embodiments generate and/or output notifications that indicate the particular computing system(s) identified as a root cause of a data event, and/or the particular computing system(s) identified not as a root cause but affected by a data event of the root cause computing system.

Embodiments of the present disclosure provide improved identification and handling of root causes for data event(s). Some embodiments improve the accuracy of determinations of a root cause or likely order of root causes of a data event affecting any number of system(s), and cause transmission of data associated with such root cause(s) for use in triaging such data event(s) and/or facilitating efficient servicing to resolve the data event. Some embodiments utilize modified centrality algorithm(s) to efficiently and accurately identify a likely root cause of a data event in a computing environment. Some embodiments generate and/or output notifications that indicate the particular computing system(s) identified as a root cause of a data event, and/or the particular computing system(s) identified not as a root cause but affected by a data event of the root cause computing system.

Disclosed in the embodiments of the present invention are an OAM message transmission method and transmission device, and a storage medium. The OAM message transmission method comprises: obtaining an OAM block generated on the basis of an OAM message; replacing an idle block in a data stream with the OAM block; and sending the data stream carrying the OAM block.

Disclosed in the embodiments of the present invention are an OAM message transmission method and transmission device, and a storage medium. The OAM message transmission method comprises: obtaining an OAM block generated on the basis of an OAM message; replacing an idle block in a data stream with the OAM block; and sending the data stream carrying the OAM block.

A method, including receiving, from multiple sources, respective sets of incidents, and respective suspiciousness labels for the incidents. A set of rules are applied so as to assign training labels to respective incidents in a subset of the incidents in the received sets. For each given incident in the subset, the respective training label is compared to the respective suspiciousness label so as to compute a respective quality score for each given source. Any sources having respective label quality scores meeting a predefined criterion are identified, and a model for computing predicted labels is fit to the incidents received from the identified sources and the respective suspiciousness labels of the incidents. The model is applied to an additional incident received from one of the sources to compute a predicted label for the additional incident, and a notification of the additional incident is prioritized in response to the predicted label.