Universal stability and cost optimization in controlled queueing networks

The control of large queueing networks is a notoriously difficult problem. Recently, an interesting new policy design framework for the control problem called h-MaxWeight has been proposed: h-MaxWeight is a natural generalization of the famous MaxWeight policy where instead of the quadratic any other surrogate value function can be applied. Stability of the policy is then achieved through a perturbation technique. However, stability crucially depends on parameter choice which has to be adapted in simulations. In this paper we use a different perturbation technique where the required properties are much easier to implement. Specifically, we derive the theoretical fundamentals which guarantee universal stability while still operating `close´ to the underlying cost criterion. Simulation examples suggest that the new approach to policy synthesis can provide significantly higher gains irrespective of any further assumptions on the network model or parameter choice.