Markovian arrival process

In queueing theory, a discipline within the mathematical theory of probability, a Markovian arrival process (MAP or MArP^[1]) is a mathematical model for the time between job arrivals to a system. The simplest such process is a Poisson process where the time between each arrival is exponentially distributed.^[2][3]

The processes were first suggested by Neuts in 1979.^[2][4]

Definition

A Markov arrival process is defined by two matrices D₀ and D₁ where elements of D₀ represent hidden transitions and elements of D₁ observable transitions. The block matrix Q below is a transition rate matrix for a continuous-time Markov chain.^[5]

$Q=\left[\begin{matrix} D_{0}&D_{1}&0&0&\dots\\ 0&D_{0}&D_{1}&0&\dots\\ 0&0&D_{0}&D_{1}&\dots\\ \vdots & \vdots & \ddots & \ddots & \ddots \end{matrix}\right]\; .$

The simplest example is a Poisson process where D₀ = −λ and D₁ = λ where there is only one possible transition, it is observable and occurs at rate λ. For Q to be a valid transition rate matrix, the following restrictions apply to the D_i

$\begin{align} 0\leq [D_{1}]_{i,j}&<\infty \\ 0\leq [D_{0}]_{i,j}&<\infty \quad i\neq j \\ \, [D_{0}]_{i,i}&<0 \\ (D_{0}+D_{1})\boldsymbol{1} &= \boldsymbol{0} \end{align}$

Special cases

Markov-modulated Poisson process

The Markov-modulated Poisson process or MMPP where m Poisson processes are switched between by an underlying continuous-time Markov chain.^[6] If each of the m Poisson processes has rate λ_i and the modulating continuous-time Markov has m × m transition rate matrix R, then the MAP representation is

$\begin{align} D_{1} &= \operatorname{diag}\{\lambda_{1},\dots,\lambda_{m}\}\\ D_{0} &=R-D_1. \end{align}$

Phase-type renewal process

The phase-type renewal process is a Markov arrival process with phase-type distributed sojourn between arrivals. For example if an arrival process has an interarrival time distribution PH$(\boldsymbol{\alpha},S)$ with an exit vector denoted $\boldsymbol{S}^{0}=-S\boldsymbol{1}$, the arrival process has generator matrix,

$Q=\left[\begin{matrix} S&\boldsymbol{S}^{0}\boldsymbol{\alpha}&0&0&\dots\\ 0&S&\boldsymbol{S}^{0}\boldsymbol{\alpha}&0&\dots\\ 0&0&S&\boldsymbol{S}^{0}\boldsymbol{\alpha}&\dots\\ \vdots&\vdots&\ddots&\ddots&\ddots\\ \end{matrix}\right]$

Batch Markov arrival process

The batch Markovian arrival process (BMAP) is a generalisation of the Markovian arrival process by allowing more than one arrival at a time.^[7] The homogeneous case has rate matrix,

$Q=\left[\begin{matrix} D_{0}&D_{1}&D_{2}&D_{3}&\dots\\ 0&D_{0}&D_{1}&D_{2}&\dots\\ 0&0&D_{0}&D_{1}&\dots\\ \vdots & \vdots & \ddots & \ddots & \ddots \end{matrix}\right]\; .$

An arrival of size $k$ occurs every time a transition occurs in the sub-matrix $D_{k}$. Sub-matrices $D_{k}$ have elements of $\lambda_{i,j}$, the rate of a Poisson process, such that,

$0\leq [D_{k}]_{i,j}<\infty\;\;\;\; 1\leq k$

$0\leq [D_{0}]_{i,j}<\infty\;\;\;\; i\neq j$

$[D_{0}]_{i,i}<0\;$

and

$\sum^{\infty}_{k=0}D_{k}\boldsymbol{1}=\boldsymbol{0}$

Fitting

A MAP can be fitted using an expectation–maximization algorithm.^[8]

Software

• KPC-toolbox a library of MATLAB scripts to fit a MAP to data.^[9]

See also

• Rational arrival process

References

1. Asmussen, S. R. (2003). "Markov Additive Models". Applied Probability and Queues. Stochastic Modelling and Applied Probability 51. pp. 302–339. doi:10.1007/0-387-21525-5_11. ISBN 978-0-387-00211-8.
2. Asmussen, S. (2000). "Matrix-analytic Models and their Analysis". Scandinavian Journal of Statistics 27 (2): 193–226. doi:10.1111/1467-9469.00186. JSTOR 4616600 – via JSTOR. (registration required (help)).
3. Chakravarthy, S. R. (2011). "Markovian Arrival Processes". Wiley Encyclopedia of Operations Research and Management Science. doi:10.1002/9780470400531.eorms0499. ISBN 9780470400531.
4. Neuts, Marcel F. (1979). "A Versatile Markovian Point Process". Journal of Applied Probability (Applied Probability Trust) 16 (4): 764–779. JSTOR 3213143 – via JSTOR. (registration required (help)).
5. Casale, G. (2011). "Building accurate workload models using Markovian arrival processes". ACM SIGMETRICS Performance Evaluation Review 39: 357. doi:10.1145/2007116.2007176.
6. Fischer, W.; Meier-Hellstern, K. (1993). "The Markov-modulated Poisson process (MMPP) cookbook". Performance Evaluation 18 (2): 149. doi:10.1016/0166-5316(93)90035-S.
7. Lucantoni, D. M. (1993). "The BMAP/G/1 queue: A tutorial". Performance Evaluation of Computer and Communication Systems. Lecture Notes in Computer Science 729. p. 330. doi:10.1007/BFb0013859. ISBN 3-540-57297-X.
8. Buchholz, P. (2003). "An EM-Algorithm for MAP Fitting from Real Traffic Data". Computer Performance Evaluation. Modelling Techniques and Tools. Lecture Notes in Computer Science 2794. pp. 218–236. doi:10.1007/978-3-540-45232-4_14. ISBN 978-3-540-40814-7.
9. Casale, G.; Zhang, E. Z.; Smirni, E. (2008). "KPC-Toolbox: Simple Yet Effective Trace Fitting Using Markovian Arrival Processes". 2008 Fifth International Conference on Quantitative Evaluation of Systems (PDF). p. 83. doi:10.1109/QEST.2008.33. ISBN 978-0-7695-3360-5.