论文标题
带有微生尾巴的随机流体网络的大偏差
Large deviations for stochastic fluid networks with Weibullian tails
论文作者
论文摘要
我们考虑了一个随机流体网络，其中外部输入过程是带有重尾的Weibullian跳跃的复合泊松。我们的结果包括对矢量值Skorokhod空间中的缓冲区内容过程的大偏差估计，该空间赋予了产品$ J_1 $拓扑。为了说明我们的框架，我们为双人队列提供明确的结果。我们证明的核心是最近的样本大偏差结果，并且在产品中$ j_1 $拓扑的Skorokhod反射图的新型连续性结果。
We consider a stochastic fluid network where the external input processes are compound Poisson with heavy-tailed Weibullian jumps. Our results comprise of large deviations estimates for the buffer content process in the vector-valued Skorokhod space which is endowed with the product $J_1$ topology. To illustrate our framework, we provide explicit results for a tandem queue. At the heart of our proof is a recent sample-path large deviations result, and a novel continuity result for the Skorokhod reflection map in the product $J_1$ topology.
