  Real-world distributed systems and networks are often unreliable and subject
to random failures of its components. Such a stochastic behavior affects
adversely the complexity of optimization tasks performed routinely upon such
systems, in particular, various resource allocation tasks. In this work we
investigate and develop Monte Carlo solutions for a class of two-stage
optimization problems in stochastic networks in which the expected value of
resource allocations before and after stochastic failures needs to be
optimized. The limitation of these problems is that their exact solutions are
exponential in the number of unreliable network components: thus, exact methods
do not scale-up well to large networks often seen in practice. We first prove
that Monte Carlo optimization methods can overcome the exponential bottleneck
of exact methods. Next we support our theoretical findings on resource
allocation experiments and show a very good scale-up potential of the new
methods to large stochastic networks.
