Abstract: Computer implemented methods, systems, and computer program products include program code executing on a processor(s) obtain factor(s) relevant to a given resource. The program code determines relationships between the factor(s). Based on parameters comprising the relationships, the program code identifies, from a search space, configuration(s) for resource(s) and configuration(s) for workload(s) in the computing environment. The program code executes, based on a pre-defined policy, a test: a workload configured according to a configuration in a system under test instance configured according to a configuration. The program code obtains performance measurements for the test in the system under test instance. The program code utilizes the performance measurements to update a known data set.

Abstract: In several aspects for generation of high quality synthetic observability data for computing systems, traces and logs from a system are collected as a seed dataset. Multiple conditional variational autoencoder (VAE) models are trained using the seed dataset for learning association between the traces and the logs. Synthetic traces and logs are generated using the multiple CVAE models while retaining the association between the traces and the logs for the synthetic traces and logs.

Abstract: An all-optical Diffractive Deep Neural Network (D2NN) architecture learns to implement various functions or tasks after deep learning-based design of the passive diffractive or reflective substrate layers that work collectively to perform the desired function or task. This architecture was successfully confirmed experimentally by creating 3D-printed D2NNs that learned to implement handwritten classifications and lens function at the terahertz spectrum. This all-optical deep learning framework can perform, at the speed of light, various complex functions and tasks that computer-based neural networks can implement, and will find applications in all-optical image analysis, feature detection and object classification, also enabling new camera designs and optical components that can learn to perform unique tasks using D2NNs. In alternative embodiments, the all-optical D2NN is used as a front-end in conjunction with a trained, digital neural network back-end.