Methods and systems for machine-learning-based resource prediction for resource allocation and anomaly detection

Abstract

A method for monitoring resources in a computing system having system information includes transforming, via representation learning, variable-size information into fixed-size information, and creating a machine learning neural network model and training it the machine learning model to predict future resource usage of an application. The method further includes providing the prediction of further resources usage of the application as an input to an action component, wherein the action component is one of an anomaly detector or a reinforcement learner that drives a scheduler. The method additionally includes performing, by the action component, at least one of scheduling resources within the computing system or detecting a resources usage anomaly.