Learning based Utility Maximization for Multi-Resource Management: Poster Abstract

This poster addresses the problem of Network Utility Maximization (NUM) where multiple resources (computing/networking) participate in user services. NUM has usually been solved by Backpressure algorithms which has to build up queue size gradualy. This disadvantage stands out in the situation of multi-resource environment or multi-hop networking. To address the problem, we propose a reinforcement learning based algorithm that utilizes future prediction to overcome the previous limitation of non-learning based algorithms.