M/D/1 queue

In queueing theory, a discipline within the mathematical theory of probability, an M/D/1 queue represents the queue length in a system having a single server, where arrivals are determined by a Poisson process and job service times are fixed (deterministic). The model name is written in Kendall's notation.^[1] Agner Krarup Erlang first published on this model in 1909, starting the subject of queueing theory.^[2]^[3] An extension of this model with more than one server is the M/D/c queue.

Model definition

An M/D/1 queue is a stochastic process whose state space is the set {0,1,2,3,...} where the value corresponds to the number of customers in the system, including any currently in service.

Arrivals occur at rate λ according to a Poisson process and move the process from state i to i + 1.
Service times are deterministic time D (serving at rate μ = 1/D).
A single server serves customers one at a time from the front of the queue, according to a first-come, first-served discipline. When the service is complete the customer leaves the queue and the number of customers in the system reduces by one.
The buffer is of infinite size, so there is no limit on the number of customers it can contain.

Stationary distribution

The number of jobs in the queue can be written as an M/G/1 type Markov chain and the stationary distribution found for state i (written π_i) in the case D = 1 to be^[4]

\begin{align}\pi_0 &= 1-\lambda \\ \pi_1 &= (1-\lambda)(e^\lambda - 1)\\ \pi_n &= (1-\lambda)\left( e^{n\lambda}+\sum_{k=1}^{n-1}e^{k\lambda}(-1)^{n-k}\left[\frac{(k\lambda)^{n-k}}{(n-k)!}+\frac{(k\lambda)^{n-k-1}}{(n-k-1)!}\right]\right) \quad (n \ge 2).\end{align}

Delay

Define ρ = λ/μ as the utilization; then the mean delay in the system in an M/D/1 queue is^[5]

\frac{1}{2\mu}\cdot\frac{2-\rho}{1-\rho}.

and in the queue:

\frac{1}{2\mu}\cdot\frac{\rho}{1-\rho}.

Busy period

The busy period is the time period measured from the instant a first customer arrives at an empty queue to the time when the queue is again empty. This time period is equal to D times the number of customers served. If ρ < 1, then the number of customers served during a busy period of the queue has a Borel distribution with parameter ρ.^[6]^[7]

Finite capacity

Stationary distribution

A stationary distribution for the number of customers in the queue and mean queue length can be computed using probability generating functions.^[8]

Transient solution

The transient solution of an M/D/1 queue of finite capacity N, often written M/D/1/N, was published by Garcia et al in 2002.^[9]

References

↑ Kendall, D. G. (1953). "Stochastic Processes Occurring in the Theory of Queues and their Analysis by the Method of the Imbedded Markov Chain". The Annals of Mathematical Statistics 24 (3): 338. doi:10.1214/aoms/1177728975. JSTOR 2236285.
↑ Kingman, J. F. C. (2009). "The first Erlang century—and the next". Queueing Systems 63: 3–4. doi:10.1007/s11134-009-9147-4.
↑ Erlang, A. K. (1909). "The theory of probabilities and telephone conversations" (PDF). Nyt Tidsskrift for Matematik B 20: 33–39. Archived from the original (PDF) on October 1, 2011.
↑ Nakagawa, Kenji (2005). "On the Series Expansion for the Stationary Probabilities of an M/D/1 queue" (PDF). Journal of the Operations Research Society of Japan 48 (2): 111–122.
↑ Cahn, Robert S. (1998). Wide Area Network Design:Concepts and Tools for Optimization. Morgan Kaufmann. p. 319. ISBN 1558604588.
↑ Tanner, J. C. (1961). "A derivation of the Borel distribution". Biometrika 48: 222–224. doi:10.1093/biomet/48.1-2.222. JSTOR 2333154.
↑ Haight, F. A.; Breuer, M. A. (1960). "The Borel-Tanner distribution". Biometrika 47: 143. doi:10.1093/biomet/47.1-2.143. JSTOR 2332966.
↑ Brun, Olivier; Garcia, Jean-Marie (2000). "Analytical Solution of Finite Capacity M/D/1 Queues". Journal of Applied Probability (Applied Probability Trust) 37 (4): 1092–1098. doi:10.1239/jap/1014843086. JSTOR 3215497.
↑ Garcia, Jean-Marie; Brun, Olivier; Gauchard, David (2002). "Transient Analytical Solution of M/D/1/N Queues". Journal of Applied Probability (Applied Probability Trust) 39 (4): 853–864. JSTOR 3216008.

Queueing theory

Single queueing nodes	D/M/1 queue M/D/1 queue M/D/c queue M/M/1 queue Burke's theorem M/M/c queue M/M/∞ queue M/G/1 queue Pollaczek–Khinchine formula Matrix analytic method M/G/k queue G/M/1 queue G/G/1 queue Kingman's formula Lindley equation Fork–join queue Bulk queue

Arrival processes	Poisson process Markovian arrival process Rational arrival process

Queueing networks	Jackson network Traffic equations Gordon–Newell theorem Mean value analysis Buzen's algorithm Kelly network G-network BCMP network

Service policies	FIFO LIFO Processor sharing Round-robin Shortest job first Shortest remaining time

Key concepts	Continuous-time Markov chain Kendall's notation Little's law Product-form solution Balance equation Quasireversibility Flow-equivalent server method Arrival theorem Decomposition method Beneš method

Limit theorems	Fluid limit Mean field theory Heavy traffic approximation Reflected Brownian motion

Extensions	Fluid queue Layered queueing network Polling system Adversarial queueing network Loss network Retrial queue

Information systems	Data buffer Erlang (unit) Erlang distribution Flow control (data) Message queue Network congestion Network scheduler Pipeline (software) Quality of service Scheduling (computing) Teletraffic engineering

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Disciplines	Actuarial mathematics Econometrics Ergodic theory Extreme value theory (EVT) Large deviations theory Mathematical finance Mathematical statistics Probability theory Queueing theory Renewal theory Ruin theory Statistics Stochastic analysis Time series analysis Machine learning

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