  This letter deals with the optimization problems of stochastic switching
buffer networks, where the switching law is governed by Markov process. The
dynamical buffer network is introduced, and its application in modeling the
car-sharing network is also presented. To address the nonconvexity for getting
a solution as close-to-the-global-optimal as possible of the optimization
problem, we adopt a succinct but effective nonconvex optimization method called
\emph{ DC (difference of convex functions) programming}. By resorting to the
log-log convexity of a class of nonlinear functions called posynomials, the
optimization problems can be reduced to DC programming problems. Finally, we
verify the effectiveness of our results by simulation experiments.
