Reinforcement-learning-based efficient resource allocation with demand-side adjustments

The problem of efficient resource allocation has drawn significant attention in many scientific disciplines due to its direct societal benefits, such as energy savings. Traditional approaches in addressing online resource allocation neglect the potential benefit of feedback information available from the running tasks/loads as well as the potential flexibility of a task to adjust its operation level in order to increase efficiency. The present paper builds upon recent developments in the area of bandwidth allocation in computing systems and proposes a design methodology for addressing a large class of online resource allocation problems with flexible tasks. The proposed methodology is based upon a measurement- or utility-based learning scheme, namely reinforcement learning. We demonstrate through analysis the potential of the proposed scheme in asymptotically providing efficient resource allocation when only measurements of the performances of the tasks are available.