Abstract: A system uses a machine learning model to identify anomalies and modify parameters of a computing environment. The system modifies parameters of a computing environment based on the presence and absence of anomalies in the computing system while avoiding modifying parameters as a result of brief spikes in computing environment attributes. The system uses a machine learning model to generate predictions of anomalies for data points of computing environment attributes. The system compiles sets of predictions into batches. The system determines whether each batch includes enough anomalous-labeled data points to be considered an anomalous batch. The system compiles the batches into sets. The system determines whether the sets of batches include enough anomalous batches to be considered an anomalous set of batches. The system modifies the parameters of the computing environment based on determining whether or not the sets of batches are anomalous.

Abstract: Techniques for cloning a computing environment while modifying components of the computing environment are disclosed. A cloning request specifies a modification to a source environment. A cloning engine determines one or more additional non-requested modifications to be made in a destination environment necessitated by the requested modification. A modified destination specification is generated including the requested modification and the one or more additional non-requested modifications. A destination stack is generated, and the destination environment is provisioned according to the destination stack.

Abstract: Techniques for cloning a computing environment while modifying components of the computing environment are disclosed. A cloning request specifies a modification to a source environment. A cloning engine determines one or more additional non-requested modifications to be made in a destination environment necessitated by the requested modification. A modified destination specification is generated including the requested modification and the one or more additional non-requested modifications. A destination stack is generated, and the destination environment is provisioned according to the destination stack.