Abstract: A system of monitoring and optimizing computing resources usage for computing application may include predicting a first performance metric for job load capacity of a computing application for optimal job concurrency and optimal resource utilization. The system may include generating an alerting threshold based on the first performance metric. The system may further include, in response to a difference between the alerting threshold and a job load of the computing application within an interval exceeding a threshold, predicting a second performance metric for job load capacity of the computing application for optimal job concurrency and optimal resource utilization. The system may further include, in response to a difference between the first performance metric and the second performance metric exceeding a difference threshold, updating the alerting threshold with a job load capacity with the optimal resource utilization rate corresponding to the second performance metric.

Abstract: Implementations include actions of receiving, by an intelligent quality assurance (iQA) platform, a desired state (DS) file including data indicative of a desired state of a cloud computing environment, triggering, by the iQA platform, an auto-discovery process to provide an actual state of the cloud computing environment based on cloud resources instantiated within the cloud environment, and application resources executing within the cloud environment, the auto-discovery process including retrieving first credentials to enable automated access to the cloud computing environment, determining, by the iQA platform, a delta between the actual state, and the desired state, and providing, by the iQA platform, a report including the delta.

Abstract: A system access a session profile. The session profile may include log source identifiers and model identifiers. The system may deploy a log projection session based on the session profile. The system may receive, in response to deployment of the log projection session, a log stream from a log source corresponding to at least one of the log identifiers. The system may generate a log projection stream based on the log stream and an initial machine-learning model. The system may calibrate the session profile and select an alternative machine-learning model based on model performance metrics. The system may redeploy the log projection session based on the calibrated session profile. The system may automatically scale computer resources for improved job performance based on forecasted log information derived from the selected machine-learning model.

Abstract: Implementations include actions of receiving, by an intelligent quality assurance (iQA) platform, a desired state (DS) file including data indicative of a desired state of a cloud computing environment, triggering, by the iQA platform, an auto-discovery process to provide an actual state of the cloud computing environment based on cloud resources instantiated within the cloud environment, and application resources executing within the cloud environment, the auto-discovery process including retrieving first credentials to enable automated access to the cloud computing environment, determining, by the iQA platform, a delta between the actual state, and the desired state, and providing, by the iQA platform, a report including the delta.