Tight matrices and heavy traffic steady state convergence in queueing networks
J.G. Dai, Yiquan Ji, Masakiyo Miyazawa

TL;DR
This paper investigates the conditions under which the reflection matrix of a semimartingale reflecting Brownian motion (SRBM) is tight, ensuring the convergence of queueing network stationary distributions to SRBM in heavy traffic, with specific results for 2D and higher dimensions.
Contribution
It provides necessary and sufficient conditions for the tightness of the reflection matrix in 2D SRBMs and sufficient conditions in higher dimensions, applied to queueing network diffusion approximations.
Findings
Tightness of R is always achieved in 2D for completely-S SRBMs.
R is tight for reentrant lines with LBFS discipline in higher dimensions.
R is not always tight for reentrant lines with FBFS discipline.
Abstract
We are interested to prove that the stationary distribution of a multiclass queueing network converges to the stationary distribution of a semimartingale reflecting Brownian motion (SRBM) in heavy traffic. A key condition for this convergence is that the sequence of the pre-limit stationary distributions under appropriate scaling is tight. In Braverman et al.(2025), a sufficient condition for this tightness is introduced in the term of the reflection matrix of the SRBM, which is coined for to be ``tight''. In this paper, we study how we can verify this tightness of of an SRBM. For a -dimensional SRBM, we give necessary and sufficient conditions for to be tight, while, for a general dimension, we only give sufficient conditions. We then apply these results to the SRBMs arising from the diffusion approximations of multiclass queueing networks with static buffer priority…
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Taxonomy
TopicsAdvanced Queuing Theory Analysis · Cloud Computing and Resource Management
