Monte-Carlo optimizations for resource allocation problems in stochastic network systems

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 this work we investigate Monte Carlo solutions for a class of two-stage optimization problems in stochastic networks in which the expected value of resources allocated before and after the oc­ curence of stochastic failures needs to be opti­ mized. The limitation of these problems is that their exact solutions are exponential in the num­ ber of unreliable network components: thus, ex­ act methods do not scale-up well to large net­ works often seen in practice. We first show that Monte Carlo optimization methods can over­ come the exponential bottleneck of exact meth­ ods. Next we support our theoretical findings on resource allocation experiments and show a very good scale-up potential of the methods on prob­ lems with large stochastic networks.