This application discloses a method and an apparatus for determining a fault type. The method includes: performing online real-time calculation on operating data generated by each of a plurality of users within a preset period, to obtain an operating feature value corresponding to the operating data generated by each of the plurality of users within the preset period; receiving a fault classification request, where the fault classification request requests to determine a fault type of a fault that is caused for a target user before a target moment; and determining, according to the fault classification request, the fault type of the fault that is caused for the target user before the target moment based on a fault classification model and an operating feature value that is of the target user within at least one preset period.

An embodiment may involve obtaining a set of data records including features characterizing operational aspects of a communication network. Each data record may include a feature vector and performance metrics of the communication network. Each feature vector may include a multiple elements corresponding to feature-value pairs. A first statistical analysis may be applied to the set of data records and their performance metrics to identify major contributors to degraded network performance. A second statistical analysis may be applied to identify elements that negatively influence the major contributors, and to discriminate between additive effects and incompatibilities as the source of negative influence. For each major contributor, a hierarchical dependency tree may be constructed with the major contributor as the root node and influencer elements as other nodes. Redundant dependencies may be removed, mutually dependent influencer elements grouped, and only the longest edges retained, in order to create dependency graph.

A system is provided herein that can correlate service issues with system telemetry associated with the software session associated with those service issues. Using a statistical approach, the system can evaluate data across numerous software sessions to rank the importance of the reported service issues. To accomplish the ranking, the system can parse the reports of service issues on a periodic basis, can extract telemetry identifiers (IDs) from the logs, can query the telemetry, may compute the relative importance of detected issues (in the context of calls going on for that day), and then can report this impact back to the service issue database.

The invention concerns a method and a system for determining root-cause diagnosis of events occurring during the operation of a communication network comprising monitoring time signals representative of the operation of the network to detect the occurrence of an event relative to the network traffic, and for each detected event, during the duration of said event obtaining distributions of data on several dimensions of the network linked to said event, automatically determining an event root-cause diagnosis of the detected event, called single event diagnosis, comprising at least one element of said distributions, an element being a value taken by a network dimension having a contribution in said distributions of data, the single event diagnosis determination using rules of business logic configuration organized hierarchically, which are applied according to said hierarchy to select at least one element of said distributions, the selection of more than one element comprising machine learning clustering.

Generating automatic diagnostics alerts is disclosed. At a first time, a set of quality metrics for a plurality of groups of streaming sessions is computed. An anomaly is identified at least in part by performing anomaly detection using the set of quality metrics and historical information. A cause of the identified anomaly is diagnosed. An alert is generated based at least in part on the diagnosis.

Described herein are improvements for generating courses of action for an information technology (IT) environment. In one example, a method includes identifying a first course of action for responding to an incident type in an information technology environment and generating a simulated incident associated with the incident type. The method further includes initiating performance of the first course of action based on the generation of the simulated incident. The method also includes, upon reaching a particular step of the first course of action that prevents the performance of the first course of action from proceeding, providing a first simulated result that allows the performance of the first course of action to proceed.

A method and system for intelligently resolving failures recurring in information technology (IT) environments. Specifically, the method and system disclosed herein may be directed to the resolution of persistently-occurring failures observed in data backup and/or data recovery operations. Further, resolution of any given persistently-occurring failure may entail the identification of zero, one, or more solutions (e.g., patches and/or other instructions) based on the analyses of failure-related information and host-related configuration information using machine learning and/or artificial intelligence paradigms. In cases where zero solutions are identified, the conventional and manual investigative route by way of support ticketing may be pursued.

In one embodiment, a network assurance service that monitors a network detects a network anomaly in the network using a machine learning-based anomaly detector. The network assurance service identifies a set of network conditions associated with the detected network anomaly. The network assurance service initiates a network test on one or more clients in the network that exhibit the identified network conditions. The network assurance service retrains the machine learning-based anomaly detector based on a result of the network test.

In one embodiment, a method comprises: detecting, by a root network device in a low power and lossy network (LLN) operating in a downward-routing mode, an outage among at least a substantial number of LLN devices in the LLN; initiating, by the root network device, a dynamic suspension of network operations in the LLN during the outage, including causing existing Internet Protocol (IP) addresses of all the LLN devices to be maintained during the outage, and causing all the LLN devices to limit transmissions to Power Outage Notification (PON) messages, Power Restoration Notification (PRN) messages, or minimal-bandwidth data packets, based on the root network device switching the LLN from the downward-routing mode to a collection-only mode; and selectively restoring, by the root network device, the LLN to the downward-routing mode in response to detecting PRN messages from at least substantially all the substantial number of LLN devices.

Examples described herein relate to method and an issue management system for handling issues reported from network devices. The issue management system may receive an issue from a network device of a plurality of network devices arranged in one or more computing environments. The issue management system may determine whether there exists a solution corresponding to the issue in a solution repository based on the issue and a knowledge base. Further, in response to determining that there exists the solution corresponding to the issue, the issue management system may communicate the solution to a computing environment of the one or more computing environments that hosts the network device reporting the issue. Alternatively, if there exists no solution corresponding to the issue, the issue management system may relay the issue to a management station for the management station to address the issue.