Abstract: Described are examples for providing service level monitoring for a network hosting applications as a cloud service. A service level monitoring device may receive end-to-end measurements of service usage collected at user devices for a plurality of applications hosted as a cloud services. The service level monitoring device may determine degraded applications of the plurality of applications based on anomalies in the measurements. The service level monitoring device may determine a service level metric based on an aggregation of the degraded applications. In some examples, the service level monitoring device may detect a network outage affecting the service.

Abstract: Operational metrics of a distributed collection of servers in a cloud environment are analyzed by a service to intelligently machine learn which operational metric is highly correlated to incidents or failures in the cloud environment. To do so, metric values of the operational metrics are analyzed over time by the service to check whether the operation metrics exceed a particular metric threshold. If so, the service also checks whether such spikes in the operation metric above the metric thresholds occurred during known cloud incidents. Statistics are calculated reflecting the number of times the operational metrics spiked during times of cloud incidents and spiked during times without cloud incidents. Correlation scores based on these statistics are calculated and used to select the correlated operational metrics that are most correlated to cloud failures.

Abstract: Described are examples for providing service level monitoring for a network hosting applications as a cloud service. A service level monitoring device may receive end-to-end measurements of service usage collected at user devices for a plurality of applications hosted as a cloud services. The service level monitoring device may determine degraded applications of the plurality of applications based on anomalies in the measurements. The service level monitoring device may determine a service level metric based on an aggregation of the degraded applications. In some examples, the service level monitoring device may detect a network outage affecting the service.

Abstract: Described are examples for providing service level monitoring for a network hosting applications as a cloud service. A service level monitoring device may receive end-to-end measurements of service usage collected at user devices for a plurality of applications hosted as a cloud services. The service level monitoring device may determine degraded applications of the plurality of applications based on anomalies in the measurements. The service level monitoring device may determine a service level metric based on an aggregation of the degraded applications. In some examples, the service level monitoring device may detect a network outage affecting the service.

Abstract: Described are examples for providing service level monitoring for a network hosting applications as a cloud service. A service level monitoring device may receive end-to-end measurements of service usage collected at user devices for a plurality of applications hosted as a cloud services. The service level monitoring device may determine degraded applications of the plurality of applications based on anomalies in the measurements. The service level monitoring device may determine a service level metric based on an aggregation of the degraded applications. In some examples, the service level monitoring device may detect a network outage affecting the service.

Abstract: Operational metrics of a distributed collection of servers in a cloud environment are analyzed by a service to intelligently machine learn which operational metric is highly correlated to incidents or failures in the cloud environment. To do so, metric values of the operational metrics are analyzed over time by the service to check whether the operation metrics exceed a particular metric threshold. If so, the service also checks whether such spikes in the operation metric above the metric thresholds occurred during known cloud incidents. Statistics are calculated reflecting the number of times the operational metrics spiked during times of cloud incidents and spiked during times without cloud incidents. Correlation scores based on these statistics are calculated and used to select the correlated operational metrics that are most correlated to cloud failures.

Abstract: Operational metrics of a distributed collection of servers in a cloud environment are analyzed by a service to intelligently machine learn which operational metric is highly correlated to incidents or failures in the cloud environment. To do so, metric values of the operational metrics are analyzed over time by the service to check whether the operation metrics exceed a particular metric threshold. If so, the service also checks whether such spikes in the operation metric above the metric thresholds occurred during known cloud incidents. Statistics are calculated reflecting the number of times the operational metrics spiked during times of cloud incidents and spiked during times without cloud incidents. Correlation scores based on these statistics are calculated and used to select the correlated operational metrics that are most correlated to cloud failures.