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Stability of Multi-Dimensional Birth-and-Death Processes with State-Dependent 0-Homogeneous Jumps

Published online by Cambridge University Press:  22 February 2016

Matthieu Jonckheere*
Affiliation:
University of Buenos Aires
Seva Shneer*
Affiliation:
Heriot-Watt University
*
Postal address: CONICET, Mathematics Department, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires (1428) Pabellón I, Ciudad Universitaria, Buenos Aires, Argentina.
∗∗ Postal address: Department of AMS, Heriot-Watt University, Edinburgh EH14 4AS, UK. Email address: [email protected]
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Abstract

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We study the conditions for positive recurrence and transience of multi-dimensional birth-and-death processes describing the evolution of a large class of stochastic systems, a typical example being the randomly varying number of flow-level transfers in a telecommunication wire-line or wireless network. First, using an associated deterministic dynamical system, we provide a generic method to construct a Lyapunov function when the drift is a smooth function on ℝN. This approach gives an elementary and direct proof of ergodicity. We also provide instability conditions. Our main contribution consists of showing how discontinuous drifts change the nature of the stability conditions and of providing generic sufficient stability conditions having a simple geometric interpretation. These conditions turn out to be necessary (outside a negligible set of the parameter space) for piecewise constant drifts in dimension two.

MSC classification

Type
Research Article
Copyright
© Applied Probability Trust 

References

Bonald, T., Massoulié, L., Proutière, A. and Virtamo, J. (2006). A queueing analysis of max-min fairness, proportional fairness and balanced fairness. Queueing Systems 53, 6584.CrossRefGoogle Scholar
Borst, S., Jonckheere, M. and Leskelä, L. (2008). Stability of parallel queueing systems with coupled service rates. Discrete Event Dyn. Syst. 18, 447472.CrossRefGoogle Scholar
Bramson, M. (2008). Stability of Queueing Networks (Lecture Notes Math. 1950). Springer, Berlin.Google Scholar
Cohen, J. W. and Boxma, O. J. (1983). Boundary Value Problems in Queueing System Analysis (North-Holland Math. Studies 79). North-Holland, Amsterdam.Google Scholar
Dai, J. G. (1995). On positive Harris recurrence of multiclass queueing networks: a unified approach via fluid limit models. Ann. Appl. Prob. 5, 4977.CrossRefGoogle Scholar
Dai, J. G. and Meyn, S. P. (1995). Stability and convergence of moments for multiclass queueing networks via fluid limit models. IEEE Trans. Automatic Control 40, 18891904.CrossRefGoogle Scholar
Dupuis, P. and Williams, R. J. (1994). Lyapunov functions for semimartingale reflecting Brownian motions. Ann. Prob. 22, 680702.CrossRefGoogle Scholar
Fayolle, G., Malyshev, V. A. and Menshikov, M. V. (1995). Topics in the Constructive Theory of Countable Markov Chains. Cambridge University Press.CrossRefGoogle Scholar
Foley, R. D. and McDonald, D. R. (2001). Join the shortest queue: stability and exact asymptotics. Ann. Appl. Prob. 11, 569607.CrossRefGoogle Scholar
Fort, G., Meyn, S., Moulines, E. and Priouret, P. (2008). The ODE method for stability of skip-free Markov chains with applications to MCMC. Ann. Appl. Prob. 18, 664707.CrossRefGoogle Scholar
Foss, S. and Konstantopoulos, T. (2004). An overview of some stochastic stability methods. J. Operat. Res. Soc. Japan 47, 275303.Google Scholar
Hirsch, M. W. and Smale, S. (1974). Differential Equations, Dynamical Systems, and Linear Algebra (Pure Appl. Math. 60). Academic Press, New York.Google Scholar
Kallenberg, O. (2002). Foundations of Modern Probability, 2nd edn. Springer, New York.CrossRefGoogle Scholar
Kelly, F. P., Maulloo, A. K. and Tan, D. K. H. (1998). Rate control for communication networks: shadow prices, proportional fairness and stability. J. Operat. Res. Soc. 49, 237252.CrossRefGoogle Scholar
Massoulié, L. (2007). Structural properties of proportional fairness: stability and insensitivity. Ann. Appl. Prob. 17, 809839.CrossRefGoogle Scholar
Meyn, S. P. (1995). Transience of multiclass queueing networks via fluid limit models. Ann. Appl. Prob. 5, 946957.CrossRefGoogle Scholar
Meyn, S. (2008). Control Techniques for Complex Networks. Cambridge University Press.Google Scholar
Meyn, S. P. and Tweedie, R. L. (1993). Markov Chains and Stochastic Stability. Springer, London.CrossRefGoogle Scholar
Rao, R. R. and Ephremides, A. (1988). On the stability of interacting queues in a multiple-access system. IEEE Trans. Inf. Theory 34, 918930.CrossRefGoogle Scholar
Robert, P. (2003). Stochastic Networks and Queues (Appl. Math. (New York) 52). Springer, Berlin.CrossRefGoogle Scholar
Rockafellar, R. T. (1970). Convex Analysis (Princeton Math. Ser. 28). Princeton University Press.CrossRefGoogle Scholar
Stolyar, A. L. (1995). On the stability of multiclass queueing networks: a relaxed sufficient condition via limiting fluid processes. Markov Process. Relat. Fields 1, 491512.Google Scholar
Szpankowski, W. (1988). Stability conditions for multidimensional queueing systems with computer applications. Operat. Res. 36, 944957.CrossRefGoogle Scholar
Szpankowski, W. (1994). Stability conditions for some distributed systems: buffered random access systems. Adv. Appl. Prob. 26, 498515.CrossRefGoogle Scholar
Tweedie, R. L. (1976). Criteria for classifying general Markov chains. Adv. Appl. Prob. 8, 737771.CrossRefGoogle Scholar