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 for governing user privacy data may include detecting a dark data source in a data ecosystem including data sources storing user privacy data. The system may further include identifying user information from the data ecosystem according to a lineage of datasets and scanning the dark data source by executing a natural language processing engine to identify existence of and content of user privacy data in the dark data source based on the user information. The system may further include selecting a target user privacy data compliance policy from user privacy data compliance policies based on a geographical region associated with the dark data source and detecting non-compliance in protecting the user privacy data in the dark data source based on the target user privacy data compliance policy. The system may further include, in response to the non-compliance, processing the user privacy data to eliminate the non-compliance.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing performance engineering are disclosed. In one aspect, a method includes the actions of providing, to a cloud computing system that is configured to implement multiple different types of virtual computing nodes, an instruction to execute an application on a virtual computing node that is a first type of virtual computing node of the multiple different types of virtual computing nodes. The actions further include determining computing performance parameters of the virtual computing node. The actions further include determining to execute the application on both the virtual computing node and an additional virtual computing node. The actions further include selecting, from among the multiple different types of virtual computing nodes, a second type of virtual computing node.

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: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing performance engineering are disclosed. In one aspect, a method includes the actions of providing, to a cloud computing system that is configured to implement multiple different types of virtual computing nodes, an instruction to execute an application on a virtual computing node that is a first type of virtual computing node of the multiple different types of virtual computing nodes. The actions further include determining computing performance parameters of the virtual computing node. The actions further include determining to execute the application on both the virtual computing node and an additional virtual computing node. The actions further include selecting, from among the multiple different types of virtual computing nodes, a second type of virtual computing node.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing performance engineering are disclosed. In one aspect, a method includes the actions of providing, to a cloud computing system that is configured to implement multiple different types of virtual computing nodes, an instruction to execute an application on a virtual computing node that is a first type of virtual computing node of the multiple different types of virtual computing nodes. The actions further include determining computing performance parameters of the virtual computing node. The actions further include determining to execute the application on both the virtual computing node and an additional virtual computing node. The actions further include selecting, from among the multiple different types of virtual computing nodes, a second type of virtual computing node.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing performance engineering are disclosed. In one aspect, a method includes the actions of providing, to a cloud computing system that is configured to implement multiple different types of virtual computing nodes, an instruction to execute an application on a virtual computing node that is a first type of virtual computing node of the multiple different types of virtual computing nodes. The actions further include determining computing performance parameters of the virtual computing node. The actions further include determining to execute the application on both the virtual computing node and an additional virtual computing node. The actions further include selecting, from among the multiple different types of virtual computing nodes, a second type of virtual computing node.

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: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing performance engineering are disclosed. In one aspect, a method includes the actions of providing, to a cloud computing system that is configured to implement multiple different types of virtual computing nodes, an instruction to execute an application on a virtual computing node that is a first type of virtual computing node of the multiple different types of virtual computing nodes. The actions further include determining computing performance parameters of the virtual computing node. The actions further include determining to execute the application on both the virtual computing node and an additional virtual computing node. The actions further include selecting, from among the multiple different types of virtual computing nodes, a second type of virtual computing node.