Abstract: To obtain one or more recommendations for the migration of a database to a cloud computing system, information about performance of the database operating under a workload may be obtained. A first machine learning model (e.g., a neural network-based autoencoder) may be used to generate a compressed representation of characteristics of the database operating under the workload. The compressed representation may then be provided as input to a second machine learning model (e.g., a neural network-based classifier), which outputs a recommendation regarding a characteristic (e.g., size, configuration, level of service) of the cloud database to which the database should be migrated. This type of recommendation may be made prior to migration, thereby making it easier to properly estimate the cost of running the cloud database and plan the migration accordingly.

Abstract: Double key encryption encrypts sensitive data using a content key, obtains a user public key from a key management service, encrypts the content key using the user public key, and encrypts the result using a cloud service provider key. Data confidentiality is protected efficiently through multilevel encryption and also by utilizing keys that are managed by different entities. Sensitivity labeling allows analytics to track sensitive data without compromising confidentiality. Compliance mechanisms may use attribute-based access control to support storage of sensitive data in a cloud, but only inside a permitted region, and without giving the cloud service provider access to the sensitive data.

Abstract: Double key encryption encrypts sensitive data using a content key, obtains a user public key from a key management service, encrypts the content key using the user public key, and encrypts the result using a cloud service provider key. Data confidentiality is protected efficiently through multilevel encryption and also by utilizing keys that are managed by different entities. Sensitivity labeling allows analytics to track sensitive data without compromising confidentiality. Compliance mechanisms may use attribute-based access control to support storage of sensitive data in a cloud, but only inside a permitted region, and without giving the cloud service provider access to the sensitive data.

Abstract: To obtain one or more recommendations for the migration of a database to a cloud computing system, information about performance of the database operating under a workload may be obtained. A first machine learning model (e.g., a neural network-based autoencoder) may be used to generate a compressed representation of characteristics of the database operating under the workload. The compressed representation may then be provided as input to a second machine learning model (e.g., a neural network-based classifier), which outputs a recommendation regarding a characteristic (e.g., size, configuration, level of service) of the cloud database to which the database should be migrated. This type of recommendation may be made prior to migration, thereby making it easier to properly estimate the cost of running the cloud database and plan the migration accordingly.