Before revision:  We then show that such an embedding is possible for more complex queueing networks whose parameters can include deterministic service times, queue capacities, and the number of servers , and empirically demonstrate that the interpolation is well behaved. To demonstrate the utility of the embedding technique, we solve a 6-parameter queuing network optimization problem by embedding the discrete parameters into a continuous space . The technique produces smooth interpolations of the objective function , and a continuous optimizer applied directly over this embedding converges rapidly, producing good solutions.
After revision:  We then show that such an embedding is possible for more complex queueing networks whose parameters can include deterministic service times, queue capacities, and the number of servers and server-delay parameters into continuous space and empirically show that the technique can produce smooth interpolations of the objective function , and a continuous optimizer applied directly over this embedding converges rapidly, producing good solutions.