Utility-function-driven resource allocation in autonomic systems

We study autonomic resource allocation among multiple applications based on optimizing the sum of utility for each application. We compare two methodologies for estimating the utility of resources: a queuing-theoretic performance model and model-free reinforcement learning. We evaluate them empirically in a distributed prototype data center and highlight tradeoffs between the two methods.