Understand the behavior of queueing networks in heavy tra c is very important due to its importance in evaluating the network performance in related applications. However, in many cases, the stationary distributions of such networks are intractable. Based on di usion limits of queueing networks, we can use Re ected Brownian Motion (RBM) processes as reasonable approximations. As such, we are interested in obtaining the stationary distribution of RBM. Unfortunately, these distributions are also in most cases intractable. However, the tail behavior (large deviations) of RBM may give insight into the stationary distribution. Assuming that a large deviations principle holds, we need only solve the corresponding variational problem to obtain the rate function. Our research is mainly focused on how to solve variational problems in the case of rotationally symmetric (RS) data. The contribution of this dissertation primarily consists of three parts. In the rst part we give out the speci c stability condition for the RBM in the octant in the RS vi case. Although the general stability conditions for RBM in the octant has been derived previously, we simplify these conditions for the case we consider. In the second part we prove that there are only two types of possible solutions for the variational problem. In the last part, we provide a simple computational method. Also we give an example under which a spiral path is the optimal solution. / text

We consider a simple one-dimensional random dynamical system with two driving vector fields and random switchings between them. We show that this system satisfies a one force - one solution principle and compute its unique invariant density explicitly. We study the limiting behavior of the invariant density as the switching rate approaches zero and infinity and derive analogues of classical probabilistic results such as the central limit theorem and large deviations principle.