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Long time asymptotics for constrained diffusions in polyhedral domains

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  • Budhiraja, Amarjit
  • Lee, Chihoon

Abstract

We study long time asymptotic properties of constrained diffusions that arise in the heavy traffic analysis of multiclass queueing networks. We first consider the classical diffusion model with constant coefficients, namely a semimartingale reflecting Brownian motion (SRBM) in a d-dimensional positive orthant. Under a natural stability condition on a related deterministic dynamical system [P. Dupuis, R.J. Williams, Lyapunov functions for semimartingale reflecting brownian motions, Annals of Probability 22 (2) (1994) 680-702] showed that an SRBM is ergodic. We strengthen this result by establishing geometric ergodicity for the process. As consequences of geometric ergodicity we obtain finiteness of the moment generating function of the invariant measure in a neighborhood of zero, uniform time estimates for polynomial moments of all orders, and functional central limit results. Similar long time properties are obtained for a broad family of constrained diffusion models with state dependent coefficients under a natural condition on the drift vector field. Such models arise from heavy traffic analysis of queueing networks with state dependent arrival and service rates.

Suggested Citation

  • Budhiraja, Amarjit & Lee, Chihoon, 2007. "Long time asymptotics for constrained diffusions in polyhedral domains," Stochastic Processes and their Applications, Elsevier, vol. 117(8), pages 1014-1036, August.
    • Handle: RePEc:eee:spapps:v:117:y:2007:i:8:p:1014-1036
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    References listed on IDEAS

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    1. Martin I. Reiman, 1984. "Open Queueing Networks in Heavy Traffic," Mathematics of Operations Research, INFORMS, vol. 9(3), pages 441-458, August.
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    Cited by:

    1. Amarjit Budhiraja & Michael Conroy & Dane Johnson, 2024. "Ergodic control of resource sharing networks: lower bound on asymptotic costs," Queueing Systems: Theory and Applications, Springer, vol. 108(3), pages 275-320, December.
    2. Yaozhong Hu & Chihoon Lee & Myung Lee & Jian Song, 2015. "Parameter estimation for reflected Ornstein–Uhlenbeck processes with discrete observations," Statistical Inference for Stochastic Processes, Springer, vol. 18(3), pages 279-291, October.
    3. Zheng Han & Yaozhong Hu & Chihoon Lee, 2016. "Optimal pricing barriers in a regulated market using reflected diffusion processes," Quantitative Finance, Taylor & Francis Journals, vol. 16(4), pages 639-647, April.
    4. Amarjit Budhiraja & Jiang Chen & Sylvain Rubenthaler, 2014. "A Numerical Scheme for Invariant Distributions of Constrained Diffusions," Mathematics of Operations Research, INFORMS, vol. 39(2), pages 262-289, May.
    5. Biswas, Anup & Budhiraja, Amarjit, 2011. "Exit time and invariant measure asymptotics for small noise constrained diffusions," Stochastic Processes and their Applications, Elsevier, vol. 121(5), pages 899-924.
    6. Andrey Sarantsev, 2017. "Reflected Brownian Motion in a Convex Polyhedral Cone: Tail Estimates for the Stationary Distribution," Journal of Theoretical Probability, Springer, vol. 30(3), pages 1200-1223, September.
    7. Amarjit Budhiraja & Xin Liu, 2012. "Stability of Constrained Markov-Modulated Diffusions," Mathematics of Operations Research, INFORMS, vol. 37(4), pages 626-653, November.
    8. Itai Gurvich, 2014. "Validity of Heavy-Traffic Steady-State Approximations in Multiclass Queueing Networks: The Case of Queue-Ratio Disciplines," Mathematics of Operations Research, INFORMS, vol. 39(1), pages 121-162, February.
    9. Lee, Chihoon, 2012. "Bounds on exponential moments of hitting times for reflected processes on the positive orthant," Statistics & Probability Letters, Elsevier, vol. 82(6), pages 1120-1128.
    10. Wenpin Tang, 2019. "Exponential ergodicity and convergence for generalized reflected Brownian motion," Queueing Systems: Theory and Applications, Springer, vol. 92(1), pages 83-101, June.
    11. Jose Blanchet & Xinyun Chen, 2020. "Rates of Convergence to Stationarity for Reflected Brownian Motion," Mathematics of Operations Research, INFORMS, vol. 45(2), pages 660-681, May.

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