Matrix geometric method

In probability theory, the matrix geometric method is a method for the analysis of quasi-birth–death processes, continuous-time Markov chain whose transition rate matrices with a repetitive block structure.^[1] The method was developed "largely by Marcel F. Neuts and his students starting around 1975."^[2]

Method description

The method requires a transition rate matrix with tridiagonal block structure as follows

${\displaystyle Q={\begin{pmatrix}B_{00}&B_{01}\\B_{10}&A_{1}&A_{2}\\&A_{0}&A_{1}&A_{2}\\&&A_{0}&A_{1}&A_{2}\\&&&A_{0}&A_{1}&A_{2}\\&&&&\ddots &\ddots &\ddots \end{pmatrix}}}$

where each of B₀₀, B₀₁, B₁₀, A₀, A₁ and A₂ are matrices. To compute the stationary distribution π writing π Q = 0 the balance equations are considered for sub-vectors π_i

${\displaystyle {\begin{aligned}\pi _{0}B_{00}+\pi _{1}B_{10}&=0\\\pi _{0}B_{01}+\pi _{1}A_{1}+\pi _{2}A_{0}&=0\\\pi _{1}A_{2}+\pi _{2}A_{1}+\pi _{3}A_{0}&=0\\&\vdots \\\pi _{i-1}A_{2}+\pi _{i}A_{1}+\pi _{i+1}A_{0}&=0\\&\vdots \\\end{aligned}}}$

Observe that the relationship

${\displaystyle \pi _{i}=\pi _{1}R^{i-1}}$

holds where R is the Neut's rate matrix,^[3] which can be computed numerically. Using this we write

${\displaystyle {\begin{aligned}{\begin{pmatrix}\pi _{0}&\pi _{1}\end{pmatrix}}{\begin{pmatrix}B_{00}&B_{01}\\B_{10}&A_{1}+RA_{0}\end{pmatrix}}={\begin{pmatrix}0&0\end{pmatrix}}\end{aligned}}}$

which can be solve to find π₀ and π₁ and therefore iteratively all the π_i.

Computation of R

The matrix R can be computed using cyclic reduction^[4] or logarithmic reduction.^[5][6]

Matrix analytic method

Main article: Matrix analytic method

The matrix analytic method is a more complicated version of the matrix geometric solution method used to analyse models with block M/G/1 matrices.^[7] Such models are harder because no relationship like π_i = π₁ R^i – 1 used above holds.^[8]

External links

• Performance Modelling and Markov Chains (part 2) by William J. Stewart at 7th International School on Formal Methods for the Design of Computer, Communication and Software Systems: Performance Evaluation

References

1. Harrison, Peter G.; Patel, Naresh M. (1992). Performance Modelling of Communication Networks and Computer Architectures. Addison-Wesley. pp. 317–322. ISBN 0-201-54419-9.
2. Asmussen, S. R. (2003). "Random Walks". Applied Probability and Queues. Stochastic Modelling and Applied Probability. Vol. 51. pp. 220–243. doi:10.1007/0-387-21525-5_8. ISBN 978-0-387-00211-8.
3. Ramaswami, V. (1990). "A duality theorem for the matrix paradigms in queueing theory". Communications in Statistics. Stochastic Models. 6: 151–161. doi:10.1080/15326349908807141.
4. Bini, D.; Meini, B. (1996). "On the Solution of a Nonlinear Matrix Equation Arising in Queueing Problems". SIAM Journal on Matrix Analysis and Applications. 17 (4): 906. doi:10.1137/S0895479895284804.
5. Latouche, Guy; Ramaswami, V. (1993). "A Logarithmic Reduction Algorithm for Quasi-Birth-Death Processes". Journal of Applied Probability. 30 (3). Applied Probability Trust: 650–674. JSTOR 3214773.
6. Pérez, J. F.; Van Houdt, B. (2011). "Quasi-birth-and-death processes with restricted transitions and its applications" (PDF). Performance Evaluation. 68 (2): 126. doi:10.1016/j.peva.2010.04.003. hdl:10067/859850151162165141.
7. Alfa, A. S.; Ramaswami, V. (2011). "Matrix Analytic Method: Overview and History". Wiley Encyclopedia of Operations Research and Management Science. doi:10.1002/9780470400531.eorms0631. ISBN 9780470400531.
8. Bolch, Gunter; Greiner, Stefan; de Meer, Hermann; Trivedi, Kishor Shridharbhai (2006). Queueing Networks and Markov Chains: Modeling and Performance Evaluation with Computer Science Applications (2 ed.). John Wiley & Sons, Inc. p. 259. ISBN 0471565253.