Abstract: Computational methods and systems described herein are directed to predicting behavior of a distributed application in response to proposed changes to the distributed application and/or proposed changes to a distributed computing system in which the distributed application is running. A training set of graphs of a distributed computing environment of the distributed application is constructed. Each graph represents a state of the distributed computing environment at a point in time. Machine learning techniques train a neural network (“NN”) model that outputs key performance indicators (“KPIs”) of the distributed application in response to changes to the distributed computing environment. When a user proposes a change, the NN model predicts KPIs that indicate how the distributed application is impacted by the proposed change.