No abstract

We revisit, in the context of Stochastic Flow Models (SFMs), a classic scheduling problem for optimally allocating a resource to multiple competing users. For the two-user case, we establish the optimality of the well-known cµ-rule for arbitrary stochastic processes using calculus of variations arguments as well as an Infinitesimal Perturbation Analysis (IPA) approach. The latter allows us to derive an explicit sensitivity estimate of the cost function with respect to a controllable parameter and to further study the problem when the cost function is nonlinear, deriving simple distribution-free cost sensitivity estimates and analyzing why the cµ-rule may fail in this case.

We consider a classic scheduling problem for optimally allocating a resource to multiple competing users and place it in the framework of Stochastic Flow Models (SFMs). We derive Infinitesimal Perturbation Analysis (IPA) gradient estimators for the average holding cost with respect to resource allocation parameters. These estimators are easily obtained from a sample path of the system without any knowledge of the underlying stochastic process characteristics. Exploiting monotonicity properties of these IPA estimators, we prove the optimality of the well-known cμ-rule for an arbitrary finite number of queues and stochastic processes under non-idling policies and linear holding costs. Further, using the IPA derivative estimates obtained along with a gradient-based optimization algorithm, we find optimal solutions to similar problems with nonlinear holding costs extending current results in the literature.

No abstract