A Fast Cross-Entropy Method for Estimating Buffer Overflows in Queueing Networks
提出一种快速自适应重要抽样方法，通过三步估计排队网络中缓冲区溢出的概率，数值实验表明该方法高效且适用于一般排队网络。
In this paper, we propose a fast adaptive importance sampling method for the efficient simulation of buffer overflow probabilities in queueing networks. The method comprises three stages. First, we estimate the minimum cross-entropy tilting parameter for a small buffer level; next, we use this as a starting value for the estimation of the optimal tilting parameter for the actual (large) buffer level. Finally, the tilting parameter just found is used to estimate the overflow probability of interest. We study various properties of the method in more detail for the M/M/1 queue and conjecture that similar properties also hold for quite general queueing networks. Numerical results support this conjecture and demonstrate the high efficiency of the proposed algorithm.