Abstract: System, methods, apparatuses, and computer program products are disclosed for auto-scaling of a deployment based on resource utilization data for a workload executing on the deployment. A resource availability is determined based on the resource utilization data and a current resource allocation of the deployment. A severity of resource throttling of the workload may be determined based on the resource utilization data, and a scaling factor is determined based at least on the severity of resource throttling. In response to at least the resource availability satisfying a predetermined condition with a predetermined threshold, the deployment is scaled based on the scaling factor.

Abstract: A code notebook and backend cloud service are configured to intelligently analyze program source code that a developer wants analyzed. A user drafts a code query to be answered about the source code that may specify specific variables, code structure elements, and/or program flows to be scrutinized. A cloud-computing environment builds a code database of the source code and analyzes its text, code structures, and program flows using. The code database is embedded with indications of semantic equivalences for text in the source code, identifications of different code structural elements, and program flows. In the cloud-computing environment, a query service takes the code query of the developer and queries the database with the machine-learned embeddings, generating query results that are shared with the developer and shown in a representation of the source code.

Abstract: A code notebook and backend cloud service are configured to intelligently analyze program source code that a developer wants analyzed. A user drafts a code query to be answered about the source code that may specify specific variables, code structure elements, and/or program flows to be scrutinized. A cloud-computing environment builds a code database of the source code and analyzes its text, code structures, and program flows. The code database is embedded with indications of semantic equivalences for text in the source code, identifications of different code structural elements, and program flows. In the cloud-computing environment, a query service takes the code query of the developer and queries the database with machine-learned embeddings, generating query results that are shared with the developer and shown in a representation of the source code.

Abstract: Performance degradation of an application that is caused by another computing process that shares infrastructure with the application is detected. The application and the other computing device may execute via different virtual machines hosted on the same computing device. To detect the performance degradation that is attributable to the other computing process, certain storage segments of a data storage (e.g., a cache) shared by the virtual machines is written with data. A pattern of read operations are then performed on the segments to determine whether an increase in read access time has occurred. Such a performance degradation is attributable to another computing process. After detecting the degradation, a metric that quantifies the detected degradation attributable to the other computing process is provided to an ML model, which determines the actual performance of the application absent the degradation attributable to the other computing process.

Abstract: Performance degradation of an application that is caused by another computing process that shares infrastructure with the application is detected. The application and the other computing device may execute via different virtual machines hosted on the same computing device. To detect the performance degradation that is attributable to the other computing process, certain storage segments of a data storage (e.g., a cache) shared by the virtual machines is written with data. A pattern of read operations are then performed on the segments to determine whether an increase in read access time has occurred. Such a performance degradation is attributable to another computing process. After detecting the degradation, a metric that quantifies the detected degradation attributable to the other computing process is provided to an ML model, which determines the actual performance of the application absent the degradation attributable to the other computing process.

Abstract: A code notebook and backend cloud service are configured to intelligently analyze program source code that a developer wants analyzed. A user drafts a code query to be answered about the source code that may specify specific variables, code structure elements, and/or program flows to be scrutinized. A cloud-computing environment builds a code database of the source code and analyzes its text, code structures, and program flows using. The code database is embedded with indications of semantic equivalences for text in the source code, identifications of different code structural elements, and program flows. In the cloud-computing environment, a query service takes the code query of the developer and queries the database with the machine-learned embeddings, generating query results that are shared with the developer and shown in a representation of the source code.