The disclosure provides methods, apparatus and computer-readable media for subscribing to, and receiving event notification messages from a CAPIF core function. In one method performed by a subscribing network entity, the subscribing network entity sends an event subscription request message to the CAPIF core function. The event subscription request message comprises an indication of an event filter to be utilized by the CAPIF core function for filtering event notifications to be sent to the subscribing network entity. The event filter comprises an identity of one or more network entities associated with events to be notified to the subscribing network entity.

Embodiments of the invention are directed to systems, method, and devices for detecting failures in distributed systems. A failure detection platform may identify anomalies in time series data, the time series data corresponding to historical network messages. The anomalies can be labeled and used to train a first predictive model. At least one other model may be trained using the time series data, the anomaly labels and a supervised machine-learning algorithm. A third model can be trained to identify a system failure based at least in part on the outputs provided by the first and the second model. The third model, once trained, can be utilized to predict a future system failure.

Example aspects include techniques for employing cross-service diagnostics for cloud service providers. These techniques may include dynamically generating a workflow of one or more diagnostic modules based on relationship information between an origin service experiencing an incident and one or more related services that the origin service depends on, and executing the workflow of one or more diagnostic modules to determine a root cause of the incident, each of the one or more diagnostic modules implemented by an individual service of the one or more related services in accordance with a schema. In addition, the techniques may include determining a diagnostic action based on the root cause, and transmitting, based on the diagnostic action, an engagement notification to a responsible entity.

The present approach relates to event monitoring and management of an instance using a generated service map, allowing monitoring of CIs (e.g., applications) and connections that are currently active in a user's specific instance. A self-monitoring solution is generated for a user (e.g., via an application) that depicts status, configuration, and errors related to the user's instance. In certain implementations, the present techniques involve applying internal knowledge of the working of a user's instance and applications to perform the self-monitoring, and determine when an alert should be generated. Further, the present techniques may involve making a determination to provide a user with a self-help solution in addition or based on the self-monitoring of the user's instance.

The present approach relates to event monitoring and management of an instance using a generated service map, allowing monitoring of CIs (e.g., applications) and connections that are currently active in a user's specific instance. A self-monitoring solution is generated for a user (e.g., via an application) that depicts status, configuration, and errors related to the user's instance. In certain implementations, the present techniques involve applying internal knowledge of the working of a user's instance and applications to perform the self-monitoring, and determine when an alert should be generated. Further, the present techniques may involve making a determination to provide a user with a self-help solution in addition or based on the self-monitoring of the user's instance.

Techniques discussed herein monitoring for and identifying failures in a cloud-computing environment. Proxy devices can be communicatively disposed between services of the cloud-computing environment. The proxy devices can capture call stack data corresponding to function calls between services. A directional graph can be generated based on that call stack data that represents the communication paths between cloud-computing resources (e.g., the services). Ingress paths to a service can be evaluated by calculating various metrics for each path. Using these metrics, failures can be isolated to a particular communication path and/or a particular, and potentially relatively distant, upstream service.

Techniques discussed herein monitoring for and identifying failures in a cloud-computing environment. Proxy devices can be communicatively disposed between services of the cloud-computing environment. The proxy devices can capture call stack data corresponding to function calls between services. A directional graph can be generated based on that call stack data that represents the communication paths between cloud-computing resources (e.g., the services). Ingress paths to a service can be evaluated by calculating various metrics for each path. Using these metrics, failures can be isolated to a particular communication path and/or a particular, and potentially relatively distant, upstream service.

A method includes processing a user input for generating a non-deterministic finite automata tree (NFAT) correlation policy. The user input indicates one or more of a static condition or a dynamic condition for inclusion in the NFAT correlation policy. The static condition includes a comparison between a defined entity and a first fixed parameter. The dynamic condition includes a comparison between the defined entity and a variable parameter. An applicable NFAT element is generated that includes at least one of the NFAT correlation policy generated based on a determination that the user input indicates the static condition or a NFAT template generated based on a determination that the user input indicates the dynamic condition. Event data received from a network device is processed to detect a status of a network entity associated with a communication network based on the applicable NFAT element.

A method includes processing a user input for generating a non-deterministic finite automata tree (NFAT) correlation policy. The user input indicates one or more of a static condition or a dynamic condition for inclusion in the NFAT correlation policy. The static condition includes a comparison between a defined entity and a first fixed parameter. The dynamic condition includes a comparison between the defined entity and a variable parameter. An applicable NFAT element is generated that includes at least one of the NFAT correlation policy generated based on a determination that the user input indicates the static condition or a NFAT template generated based on a determination that the user input indicates the dynamic condition. Event data received from a network device is processed to detect a status of a network entity associated with a communication network based on the applicable NFAT element.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive a warning message comprising a first indication of a priority of the warning message and a second indication that identifies an event. The UE may transmit a repetition of the warning message based at least in part on the first indication. Numerous other aspects are described.