An Approximation to the Invariant Measure of the Limiting Diffusion of G/Ph/n + GI Queues in the Halfin–Whitt Regime and Related Asymptotics

Author:

Jin Xinghu123ORCID,Pang Guodong4ORCID,Xu Lihu23ORCID,Xu Xin5ORCID

Affiliation:

1. School of Mathematics, Hefei University of Technology, Hefei, Anhui 230601, China;

2. Department of Mathematics, Faculty of Science and Technology, University of Macau, Taipa, Macau 999078, China;

3. The University of Macau’s Zhuhai Research Institute, Zhuhai, Guangdong 519000, China;

4. Department of Computational Applied Mathematics and Operations Research, George R. Brown School of Engineering, Rice University, Houston, Texas 77005;

5. School of Mathematical Sciences, South China Normal University, Guangdong 510631, China

Abstract

In this paper, we develop a stochastic algorithm based on the Euler–Maruyama scheme to approximate the invariant measure of the limiting multidimensional diffusion of [Formula: see text] queues in the Halfin–Whitt regime. Specifically, we prove a nonasymptotic error bound between the invariant measures of the approximate model from the algorithm and the limiting diffusion. To establish the error bound, we employ the recently developed Stein’s method for multidimensional diffusions, in which the regularity of Stein’s equation obtained by the partial differential equation (PDE) theory plays a crucial role. We further prove the central limit theorem (CLT) and the moderate deviation principle (MDP) for the occupation measures of the limiting diffusion of [Formula: see text] queues and its Euler–Maruyama scheme. In particular, the variances in the CLT and MDP associated with the limiting diffusion are determined by Stein’s equation and Malliavin calculus, in which properties of a mollified diffusion and an associated weighted occupation time play a crucial role. Funding: X. Jin is supported in part by the Fundamental Research Funds for the Central Universities [Grants JZ2022HGQA0148 and JZ2023HGTA0170]. G. Pang is supported in part by the U.S. National Science Foundation [Grants DMS-1715875 and DMS-2216765]. L. Xu is supported in part by the National Nature Science Foundation of China [Grant 12071499], Macao Special Administrative Region [Grant FDCT 0090/2019/A2], and the University of Macau [Grant MYRG2018-00133-FST]. This work was supported by U.S. National Science Foundation [Grant DMS-2108683].

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

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