Abstract: Certain aspects of the present disclosure provide techniques for detecting and preventing web service usage anomalies. Embodiments include forecasting, based on a model, a number of resource instances for one or more web services for a time period. Embodiments include receiving, after the time period has elapsed, a detected number of resource instances for the one or more web services for the time period. Embodiments include comparing the detected number of resource instances to the forecasted number of resource instances. Embodiments include determining, based on the comparing, a usage anomaly. Embodiments include determining an impact of the usage anomaly. Embodiments include outputting an indication of the usage anomaly and the impact of the usage anomaly.

Abstract: Certain aspects of the present disclosure provide techniques for detecting and preventing web service usage anomalies. Embodiments include forecasting, based on a model, a number of resource instances for one or more web services for a time period. Embodiments include receiving, after the time period has elapsed, a detected number of resource instances for the one or more web services for the time period. Embodiments include comparing the detected number of resource instances to the forecasted number of resource instances. Embodiments include determining, based on the comparing, a usage anomaly. Embodiments include determining an impact of the usage anomaly. Embodiments include outputting an indication of the usage anomaly and the impact of the usage anomaly.

Abstract: Classifying a problem associated with an application may include receiving a log including a record of at least one operation performed by at least one application. The classifying may include generating a vector based on the record. The classifying may include comparing the vector to a plurality of trained vectors each being associated with a separate label. The classifying may include determining a most similar one of the plurality of the trained vectors to the vector based on the comparing. The classifying may include assigning the label associated with the most similar one of the plurality of trained vectors to the vector. The classifying may include selecting a recipient from among a plurality of available recipients based on the label assigned by the processor. The classifying may include causing the log to be further processed by the recipient.

Abstract: A system and method for use with a data management service that provides a finite state machine with machine learning based generation of tests to ensure exhaustive coverage of the compound states of an application undergoing modification. The finite state machine is modified with inputs of feedback from production usage of the application. A continuous virtual test pool utilizes the finite state machine to replicate numerous instances of users simulating the possible states of the application.