Abstract: Embodiments of the disclosure include a method for building a tunable sizing model for software sizing recommendation to operate on computer systems. The method includes building a sizing machine learning model that is trained in accordance with an output of a sizing application. The sizing machine learning model is further trained with data of a target system. The sizing machine learning model is configured to output a sizing result for a software. The method includes executing the sizing machine learning model based on feedback from the target system to output the sizing result. The method includes causing the software to be modified according to the sizing result, the software being configured for execution on the target system.

Abstract: In some examples, a circuit includes a first power converter cell and a second power converter cell. The first power converter cell has a first bidirectional interface. The first power converter cell is configured to switch power from the first bidirectional interface to a second bidirectional interface in a first operation mode. The second power converter cell has a third bidirectional interface. The second power converter cell is configured to switch power from the third bidirectional interface to the second bidirectional interface in the first operation mode in parallel with the first power converter cell.

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: Embodiments monitor a target system to collect at least one data metric; pre-process the at least one data metric as a seed based on a predetermined policy; encode the pre-processed seed using a transform; post-process the encoded seed in a frequency domain; generate synthetic metrics data by applying an inverse transform to the post-processed seed; and train an artificial intelligence (AI) model using the generated synthetic metrics data.

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: Self-tuning merged code testing is provided which includes testing merged code using a suite of test cases, where the merged code includes one or more code changes, and obtaining, based on the testing, a test case failure using the suite of test cases. Further, the process includes determining, using an artificial intelligence engine, a likely faulty code change of the one or more code changes resulting in the test case failure, and customizing, based on the likely faulty code change, the suite of test cases to facilitate verifying that the likely faulty code change is a faulty code change. In addition, the process includes continuing testing of the merged code using the customized suite of test cases to facilitate verifying that the likely faulty code change is the faulty code change.