Abstract: A computer-implemented method includes identifying static information associated with a computing device that is running a media playback application. The method also includes monitoring the media playback application during a playback session to identify dynamic information associated with the playback session. Still further, the method includes instantiating a trained machine learning (ML) model to determine, based on historical usage data associated with the media playback application, a likelihood that the media playback application will experience an out of memory kill. The trained ML model implements a graded window to indicate a scaled likelihood that an out of memory kill will occur within a specified timeframe. Then, according to the trained ML model's determination, the method generates a prediction that an out of memory kill will occur for the media playback application within the specified timeframe. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A system and method for generating a simulation environment are provided. In example aspects, in response to a request from a user of an environment simulation system, specifying a desired web performance metric to result from the execution of a particular content item, the environment simulation system constructs a simulation environment. The environment simulation system is configured to test the particular content item within the constructed simulation environment and compare a baseline web performance metric to the requested web performance metric. In some aspects, if the requested web performance metric is not achieved, the environmental simulation system is configured to perform tuning and continue simulating until the requested web performance metric is achieved. Once tuning is complete, a user can modify a content item and continue simulating to observe improvements in web performance of a content item for a user in a particular geographic region.

Abstract: A system and method for generating a simulation environment are provided. In example aspects, in response to a request from a user of an environment simulation system, specifying a desired web performance metric to result from the execution of a particular content item, the environment simulation system constructs a simulation environment. The environment simulation system is configured to test the particular content item within the constructed simulation environment and compare a baseline web performance metric to the requested web performance metric. In some aspects, if the requested web performance metric is not achieved, the environmental simulation system is configured to perform tuning and continue simulating until the requested web performance metric is achieved. Once tuning is complete, a user can modify a content item and continue simulating to observe improvements in web performance of a content item for a user in a particular geographic region.