Abstract: The present disclosure provides an experimentation framework for a computational environment in a distributed system. A machine-learning model may be created that predicts at least one output produced by the computational environment based on at least one input provided to the computational environment. During an evaluation time period that is subsequent to at least one modification being made to the computational environment, at least one modified output produced by the computational environment may be determined. The machine-learning model may be used to calculate at least one predicted output that would have been produced by the computational environment during the evaluation time period if the at least one modification had not been made. A determination may also be made about how the at least one modification affected the computational environment based on a comparison of the at least one modified output and the at least one predicted output.

Abstract: The present disclosure provides an experimentation framework for a computational environment in a distributed system. A machine-learning model may be created that predicts at least one output produced by the computational environment based on at least one input provided to the computational environment. During an evaluation time period that is subsequent to at least one modification being made to the computational environment, at least one modified output produced by the computational environment may be determined. The machine-learning model may be used to calculate at least one predicted output that would have been produced by the computational environment during the evaluation time period if the at least one modification had not been made. A determination may also be made about how the at least one modification affected the computational environment based on a comparison of the at least one modified output and the at least one predicted output.

Abstract: The present disclosure provides an experimentation framework for a computational environment in a distributed system. A machine-learning model may be created that predicts at least one output produced by the computational environment based on at least one input provided to the computational environment. During an evaluation time period that is subsequent to at least one modification being made to the computational environment, at least one modified output produced by the computational environment may be determined. The machine-learning model may be used to calculate at least one predicted output that would have been produced by the computational environment during the evaluation time period if the at least one modification had not been made. A determination may also be made about how the at least one modification affected the computational environment based on a comparison of the at least one modified output and the at least one predicted output.

Abstract: The present disclosure provides an experimentation framework for a computational environment in a distributed system. A machine-learning model may be created that predicts at least one output produced by the computational environment based on at least one input provided to the computational environment. During an evaluation time period that is subsequent to at least one modification being made to the computational environment, at least one modified output produced by the computational environment may be determined. The machine-learning model may be used to calculate at least one predicted output that would have been produced by the computational environment during the evaluation time period if the at least one modification had not been made. A determination may also be made about how the at least one modification affected the computational environment based on a comparison of the at least one modified output and the at least one predicted output.

Abstract: Examples of the present disclosure describe security context enforcement in a multi-tenant environment. Security context data may be transmitted through an un-secure multi-tenant computational environment. The security context data is secured by protection layers that restrict untrusted resources from running tenant applications and restrict the ability of unauthorized tenants to access context information associated with a tenant. Data may be received and evaluated at a component of a multi-tenant environment. If the component is a trusted component and the security context data indicates that the tenant is authorized to execute an application using a specified context, the component may run a tenant application in a context associated with the security context data.

Abstract: Examples of the present disclosure describe security context enforcement in a multi-tenant environment. Security context data may be transmitted through an un-secure multi-tenant computational environment. The security context data is secured by protection layers that restrict untrusted resources from running tenant applications and restrict the ability of unauthorized tenants to access context information associated with a tenant. Data may be received and evaluated at a component of a multi-tenant environment. If the component is a trusted component and the security context data indicates that the tenant is authorized to execute an application using a specified context, the component may run a tenant application in a context associated with the security context data.