  We study the design of dynamic scheduling controls in closed queueing
networks with a fixed number of jobs. Each time a server becomes available, the
controller has (limited) flexibility in choosing the buffer from which to serve
a job. If no jobs are available at any compatible buffer, the server idles. If
the job is served, it relocates to a ``destination'' buffer. We study how to
maximize throughput in steady state via a large deviations analysis.
  We propose a family of simple state-dependent policies called Scaled
MaxWeight (SMW) policies that dynamically manage the distribution of jobs in
the network. We prove that under a complete resource pooling condition
(analogous to the condition in Hall's marriage theorem), any SMW policy leads
to exponential decay of throughput-loss probability as the number of jobs
scales to infinity. Further, there is an SMW policy that achieves the optimal
loss exponent among all scheduling policies, and we analytically specify this
policy in terms of the service rates and routing probabilities. The optimal SMW
policy maintains high job levels adjacent to structurally under-supplied
servers. Our methodology also applies to the open network setting and leads to
exponent-optimal policies.
