Hostname: page-component-8448b6f56d-c4f8m Total loading time: 0 Render date: 2024-04-18T03:39:52.880Z Has data issue: false hasContentIssue false

THE INVARIANT MEASURE OF RANDOM WALKS IN THE QUARTER-PLANE: REPRESENTATION IN GEOMETRIC TERMS

Published online by Cambridge University Press:  26 January 2015

Yanting Chen
Affiliation:
Stochastic Operations Research, University of Twente The Netherlands E-mails: y.chen@utwente.nl, r.j.boucherie@utwente.nl
Richard J. Boucherie
Affiliation:
Stochastic Operations Research, University of Twente The Netherlands E-mails: y.chen@utwente.nl, r.j.boucherie@utwente.nl
Jasper Goseling
Affiliation:
Department of Intelligent Systems, Delft University of Technology The Netherlands;Stochastic Operations Research, University of Twente The Netherlands E-mail: j.goseling@utwente.nl

Abstract

We consider the invariant measure of homogeneous random walks in the quarter-plane. In particular, we consider measures that can be expressed as a finite linear combination of geometric terms and present conditions on the structure of these linear combinations such that the resulting measure may yield an invariant measure of a random walk. We demonstrate that each geometric term must individually satisfy the balance equations in the interior of the state space and further show that the geometric terms in an invariant measure must have a pairwise-coupled structure. Finally, we show that at least one of the coefficients in the linear combination must be negative.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Adan, I.J.B.F., van Houtum, G.J., Wessels, J. & Zijm, W.H.M. (1993). A compensation procedure for multiprogramming queues. OR Spectrum, 15(2): 95106.Google Scholar
2.Adan, I.J.B.F., Wessels, J. & Zijm, W.H.M. (1993). A compensation approach for two-dimensional Markov processes. Advances in Applied Probability, 25(4): 783817.Google Scholar
3.Boucherie, R.J. & van Dijk, N.M. (eds.) (2010). Queueing networks: a fundamental approach. New York: Springer-Verlag.Google Scholar
4.Cohen, J.W. & Boxma, O.J. (1983). Boundary value problems in queueing system analysis. Amsterdam: North-Holland.Google Scholar
5.Dieker, A.B. & Moriarty, J. (2009). Reflected Brownian motion in a wedge: sum-of-exponential stationary densities. Electronic Communications in Probability, 14: 116.Google Scholar
6.van Dijk, N.M. & Puterman, M.L. (1988). Perturbation theory for Markov reward processes with applications to queueing systems. Advances in Applied Probability, 20: 7998.CrossRefGoogle Scholar
7.Fayolle, G., Iasnogorodski, R. & Malyshev, V.A. (1999). Random walks in the quarter-plane: algebraic methods, boundary value problems and applications. New York: Springer-Verlag.Google Scholar
8.Goseling, J., Boucherie, R.J. & Ommeren, J.C.W. (2012). Linear programming error bounds for random walks in the quarter-plane. Memorandum 1988, Department of Applied Mathematics, University of Twente, The Netherlands.Google Scholar
9.Miyazawa, M. (2011). Light tail asymptotics in multidimensional reflecting processes for queueing networks. Top, 19: 233299.Google Scholar
10.Müller, A. & Stoyan, D. (2002). Comparison methods for stochastic models and risks. New York: Wiley.Google Scholar
11.Neuts, M.F. (1981). Matrix-geometric solutions in stochastic models: an algorithmic approach. Mineola: Dover Publications.Google Scholar
12.Wolff, R.W. (1989). Stochastic modeling and the theory of queues. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar