JUSTIFYING DIFFUSION APPROXIMATIONS FOR MULTICLASS QUEUEING NETWORKS UNDER A MOMENT CONDITION

• 成果类型: Article
• 署名作者: Ye, Heng-Qing; Yao, David D.
• 署名单位: Hong Kong Polytechnic University; Columbia University
• 刊物名称: ANNALS OF APPLIED PROBABILITY
• ISSN/ISSBN: 1050-5164
• DOI: 10.1214/18-AAP1401
• 发表日期: 2018
• 页码: 3652-3697
• 关键词: state-space collapse; heavy-traffic optimality; fluid limit models; Asymptotic Optimality; service disciplines; Stochastic Network; CONVERGENCE; stationarity; STABILITY; equilibria

摘要

Multiclass queueing networks (MQN) are, in general, difficult objects to study analytically. The diffusion approximation refers to using the stationary distribution of the diffusion limit as an approximation of the diffusion-scaled process (say, the workload) in the original MQN. To validate such an approximation amounts to justifying the interchange of two limits, t -> infinity and k -> infinity, with t being the time index and k, the scaling parameter. Here, we show this interchange of limits is justified under a p*th moment condition on the primitive data, the interarrival and service times; and we provide an explicit characterization of the required order (p*), which depends naturally on the desired order of moment of the workload process.