Abstract
We study the relation of Markov fluid queues and QBD processes in this paper. Ahn and Ramaswami presented results about this relation and provided a stochastic interpretation based reasoning in [1]. In the current work, first we provide an algebraic proof for that relation.
After that, we present a negative result about the potential extension of the QBD based analysis Markov fluid queues to Markov fluid queues with two buffers. We present a 2-dimensional QBD process, which could be a candidate for describing the stationary behaviour of the related Markov fluid queue, but it turns out that the QBD based behaviour is different from the one of the Markov fluid queue.
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References
Ahn, S., Ramaswami, V.: Fluid flow models and queues - a connection by stochastic coupling. Stoch. Model. 19(3), 325–348 (2003)
Akar, N., Sohraby, K.: An invariant subspace approach in \(M/G/1\) and \(G/M/1\) type Markov chains. Stoch. Model. 13(3), 381–416 (1997)
Akar, N., Sohraby, K.: Infinite-and finite-buffer Markov fluid queues: a unified analysis. J. Appl. Probability, 557–569 (2004)
Anick, D., Mitra, D., Sondhi, M.M.: Stochastic theory of a data-handling system. Bell Sys. Thech. J. 61(8), 1871–1894 (1982)
Asmussen, S.: Stationary distributions for fluid flow models with or without Brownian noise. Stoch. Model. 11(1), 21–49 (1995)
Bean, N.G., Lewis, A., Nguyen, G.T., O’Reilly, M.M., Sunkara, V.: A discontinuous galerkin method for approximating the stationary distribution of stochastic fluid-fluid processes. Methodol. Comput. Appl. Probab. 24(4), 2823–2864 (2022)
Bean, N.G., O’Reilly, M.M.: The stochastic fluid-fluid model: a stochastic fluid model driven by an uncountable-state process, which is a stochastic fluid model itself. Stochastic Processes Appl. 124(5), 1741–1772 (2014)
Bean, N.G., O’Reilly, M.M., Palmowski, Z.: Matrix-analytic methods for the analysis of stochastic fluid-fluid models. Stoch. Model. 38(3), 416–461 (2022)
Bright, L., Taylor, P.G.: Calculating the equilibrium distribution in level dependent quasi-birth-and-death processes. Stoch. Model. 11(3), 497–525 (1995)
Buchholz, P., Mészáros, A., Telek, M.: Analysis of a two-state markov fluid model with 2 buffers. In: Iacono, M., Scarpa, M., Barbierato, E., Serrano, S., Cerotti, D., Longo, F. (eds.) Computer Performance Engineering and Stochastic Modelling - 19th European Workshop, EPEW 2023, and 27th International Conference, ASMTA 2023, Florence, Italy, June 20-23, 2023, Proceedings, vol. 14231, pp. 49–64. Springer, Cham (2023. https://doi.org/10.1007/978-3-031-43185-2_4
Buchholz, P., Mészáros, A., Telek, M.: Stationary analysis of a constrained markov fluid model with two buffers. Stochastic Models (2024). (to appear)
da Silva Soares, A., Latouche, G.: Matrix-analytic methods for fluid queues with finite buffers. Performance Eval. 63(4-5), 295–314 (2006)
Hohn, N., Veitch, D., Papagiannaki, K., Diot, C.: Bridging router performance and queuing theory. SIGMETRICS Perform. Eval. Rev. 32(1), 355–366 (2004)
Karandikar, R.L., Kulkarni, V.G.: Second-order fluid flow models: reflected Brownian motion in a random environment. Oper. Res. 43, 77–88 (1995)
Latouche, G., Ramaswami, V.: Introduction to matrix analytic methods in stochastic modeling. SIAM (1999)
O’Reilly, M.M., Scheinhardt, W.: Stationary distributions for a class of Markov-modulated tandem fluid queues. Stoch. Model. 33(4), 524–550 (2017)
Ramaswami, V.: Matrix analytic methods for stochastic fluid flows. In: International Teletraffic Congress, pp. 1019–1030, Edinburg (1999)
Stanford, D.A., Latouche, G., Woolford, D.G., Boychuk, D., Hunchak, A.: Erlangized fluid queues with application to uncontrolled fire perimeter. Stoch. Model. 21(2–3), 631–642 (2005)
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A Proof of Theorem 2
A Proof of Theorem 2
Using \(\textbf{P}_{++}=\textbf{Q}_{++}/\lambda +\textbf{I}\), \(\textbf{P}_{+-}=\textbf{Q}_{+-}/\lambda \), \(\textbf{P}_{--}=\textbf{Q}_{--}/\lambda +\textbf{I}\), \(\textbf{P}_{-+}=\textbf{Q}_{-+}/\lambda \), for \(i \ge j \ge 1\), Equations (45)–(50) can be simplified to
Multiplying (60) by \(\frac{\lambda ^{i} x^{i-1} e^{-\lambda x}}{(i-1)!}\) and summing up from \(i=1\) to \(\infty \) gives
which results in (51).
By definition \(\tilde{W}(0,x)^\pm =\sum _{i=1}^{\infty } \frac{\lambda ^i x^{i-1} e^{-\lambda x}}{(i-1)!} \lambda p_{i,1}^\pm .\) Multiplying (56) by \(\frac{\lambda ^i x^{i-1} e^{-\lambda x}}{(i-1)!}\) and summing up from \(i=1\) to \(\infty \) gives
which results in (51).
For the computation of \(\tilde{W}(x,y)\) we need the following lemma.
Lemma 1
The derivatives of \(\tilde{W}(x,y)^\pm =\sum _{i=1}^{\infty } \sum _{j=1}^{i} \frac{\lambda ^i y^{i-1} e^{-\lambda y}}{(i-1)!} \frac{\lambda ^j x^{j-1} e^{-\lambda x}}{(j-1)!} p_{i,j}^\pm \) satisfy
and
Proof
The statements of the lemma can be obtained by substituting the definition of \(\tilde{W}(x,y)^\pm \). We omit the details of the proof here.
Multiplying (57) by \(\frac{\lambda ^{i-1} y^{i-2} e^{-\lambda y}}{(i-2)!}\frac{\lambda ^{j-1} x^{j-2} e^{-\lambda x}}{(j-2)!}\) and summing up from \(i=2\) to \(\infty \) and \(j=2\) to i and utilizing (66) gives
which results in (53).
For \(2\le j \le i\) we rewrite (58) as
Multiplying (67) by \(\frac{\lambda ^{i-1} y^{i-2} e^{-\lambda y}}{(i-2)!}\frac{\lambda ^{j-1} x^{j-2} e^{-\lambda x}}{(j-2)!}\) and summing up from \(i=2\) to \(\infty \) and \(j=2\) to i and utilizing Lemma 1 gives
that is
which results in (54).
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Buchholz, P., Meszaros, A., Telek, M. (2025). An Algebraic Proof of the Relation of Markov Fluid Queues and QBD Processes. In: Devos, A., Horváth, A., Rossi, S. (eds) Analytical and Stochastic Modelling Techniques and Applications. ASMTA 2024. Lecture Notes in Computer Science, vol 14826. Springer, Cham. https://doi.org/10.1007/978-3-031-70753-7_9
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