1. An automated method for allocating resources among a plurality of resource-using computational entities in a data processing system, the method comprising: establishing a service-level utility for each of said plurality of resource-using computational entities; and transforming said service-level utility into a resource-level utility for each of said plurality of resource-using computational entities, wherein the resource-level utility is representative of an amount of business value obtained by each of said plurality of resource-using computational entities when a quantity of said resources is allocated to the each of said plurality of resource-using computational entities, wherein the resource-level utility indicates, for at least one of said plurality of resource-using computational entities, an estimated cumulative discounted or undiscounted future utility starting from current state descriptions of said at least one of said plurality of resource-using computational entities, wherein the estimated cumulative discounted or undiscounted future utility is trained on a temporal sequence of observed data using an adaptive machine learning procedure, wherein the machine learning procedure is a reinforcement learning procedure, and wherein the reinforcement learning procedure is Q-Learning, Temporal Difference Learning, R-Learning or SARSA.