M/G/k queue: Difference between revisions

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In queueing theory, an M/G/k queue is a queue model where arrivals are Markovian (modulated by a Poisson process), service times have a General distribution and there are k servers. The model name is written in Kendall's notation, and is an extension of the M/M/c queue, where service times must be exponentially distributed.

Model definition

A queue represented by a M/G/k queue is a stochastic process whose state space is the set {0,1,2,3...}, where the value corresponds to the number of members in the queue, including any being served. Transitions from state i to i + 1 represent the arrival of a new queue member: the times between such arrivals have an exponential distribution with parameter λ. Transitions from state i to i − 1 represent a member who has been being served, finishing being served and departing: the length of time required for serving an individual queue member has a general distribution function. The lengths of times between arrivals and of service periods are random variables which are assumed to be statistically independent.

Results

Results for an extended version of this problem, an M/G/k queue with k > 1 servers remains an open problem.^[1] In this model, arrivals are determined by a Poisson process and jobs can be served by any one of k servers. Tijms et al. believe it is "not likely that computationally tractable methods can be developed to compute the exact numerical values of the steady-state probability in the M/G/k queue."^[2]

Various approximations for the average queue size,^[3] average delay a job experiences^[2] (where it is known no approximation using only the first two moments can be accurate in all cases^[4] and a Markov–Krein characterisation has been shown to produce tight bounds^[5]), stationary distribution^[6]^[7] and approximation by a reflected Brownian motion^[8]^[9] have been offered by different authors. See the M/M/c queue article for the case where service times are exponentially distributed.

M/G/2 queue

For an M/G/2 queue, the model with two servers, the problem of determining marginal probabilities can be reduced to solving a pair of integral equations^[10] or the Laplace transform of the distribution when the service time distribution is a mixture of exponential distributions.^[11] The Laplace transform of queue length^[12] and waiting time distributions^[13] can be computed when the waiting time distribution has a rational Laplace transform.

References

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+In [[queueing theory]], an '''M/G/k queue''' is a queue model where arrivals are '''M'''arkovian (modulated by a [[Poisson process]]), service times have a '''G'''eneral [[probability distribution|distribution]] and there are ''k'' servers. The model name is written in [[Kendall's notation]], and is an extension of the [[M/M/c queue]], where service times must be [[exponential distribution|exponentially distributed]].
-#REDIRECT[[M/G/1 queue]]
+==Model definition==
+A queue represented by a M/G/''k'' queue is a stochastic process whose [[state space]] is the set {0,1,2,3...}, where the value corresponds to the number of members in the queue, including any being served. Transitions from state ''i'' to ''i''&nbsp;+&nbsp;1 represent the arrival of a new queue member: the times between such arrivals have an [[exponential distribution]] with parameter λ. Transitions from state ''i'' to ''i''&nbsp;&minus;&nbsp;1 represent a member who has been being served, finishing being served and departing: the length of time required for serving an individual queue member has a general distribution function. The lengths of times between arrivals and of service periods are [[random variable]]s which are assumed to be [[statistically independent]].
+==Results==
+Results for an extended version of this problem, an M/G/''k'' queue with ''k''&nbsp;>&nbsp;1 servers remains an open problem.<ref>{{cite doi|10.1007/s11134-009-9147-4}}</ref> In this model, arrivals are determined by a [[Poisson process]] and jobs can be served by any one of ''k'' servers. Tijms ''et al.'' believe it is "not likely that computationally tractable methods can be developed to compute the exact numerical values of the steady-state probability in the M/G/''k'' queue."<ref name="tijms">{{cite jstor|1426474}}</ref>
+Various approximations for the average queue size,<ref>{{cite doi|10.1287/opre.43.1.158}}</ref> average delay a job experiences<ref name="tijms" /> (where it is known no approximation using only the first two moments can be accurate in all cases<ref>{{cite doi|10.1007/s11134-009-9133-x}}</ref> and a [[Markov–Krein]] characterisation has been shown to produce tight bounds<ref>{{cite doi|10.1007/s11134-011-9248-8}}</ref>), stationary distribution<ref>{{cite doi|10.1007/s11134-008-9073-x}}</ref><ref>{{cite jstor|169760}}</ref> and approximation by a [[reflected Brownian motion]]<ref>{{cite doi|10.1287/opre.31.2.304}}</ref><ref>{{cite doi|10.1287/opre.33.6.1266}}</ref> have been offered by different authors. See the [[M/M/c queue]] article for the case where service times are exponentially distributed.
+===M/G/2 queue===
+For an M/G/2 queue, the model with two servers, the problem of determining marginal probabilities can be reduced to solving a pair of [[integral equation]]s<ref>{{cite doi|10.1287/opre.38.3.506}}</ref> or the Laplace transform of the distribution when the service time distribution is a mixture of exponential distributions.<ref>{{cite doi|10.1016/0304-4149(82)90046-1}}</ref> The Laplace transform of queue length<ref>{{cite jstor|1426776}}</ref> and waiting time distributions<ref>{{cite doi|10.1023/A:1017913826973}}</ref> can be computed when the waiting time distribution has a rational Laplace transform.
+==References==
+{{Reflist}}
+{{Queueing theory}}
+{{Stochastic processes}}
+{{DEFAULTSORT:M G k queue}}
+[[Category:Single queueing nodes]]
+[[Category:Stochastic processes]]

v t e 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 point 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 next 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
Category

v t e Stochastic processes
Discrete time	Bernoulli process Branching process Chinese restaurant process Galton–Watson process Independent and identically distributed random variables Markov chain Moran process Random walk Loop-erased Self-avoiding Biased Maximal entropy
Continuous time	Additive process Bessel process Birth–death process pure birth Brownian motion Bridge Excursion Fractional Geometric Meander Cauchy process Contact process Continuous-time random walk Cox process Diffusion process Dyson Brownian motion Empirical process Feller process Fleming–Viot process Gamma process Geometric process Hawkes process Hunt process Interacting particle systems Itô diffusion Itô process Jump diffusion Jump process Lévy process Local time Markov additive process McKean–Vlasov process Ornstein–Uhlenbeck process Poisson process Compound Non-homogeneous Schramm–Loewner evolution Semimartingale Sigma-martingale Stable process Superprocess Telegraph process Variance gamma process Wiener process Wiener sausage
Both	Branching process Gaussian process Hidden Markov model (HMM) Markov process Martingale Differences Local Sub- Super- Random dynamical system Regenerative process Renewal process Stochastic chains with memory of variable length White noise
Fields and other	Dirichlet process Gaussian random field Gibbs measure Hopfield model Ising model Potts model Boolean network Markov random field Percolation Pitman–Yor process Point process Cox Poisson Random field Random graph
Time series models	Autoregressive conditional heteroskedasticity (ARCH) model Autoregressive integrated moving average (ARIMA) model Autoregressive (AR) model Autoregressive–moving-average (ARMA) model Generalized autoregressive conditional heteroskedasticity (GARCH) model Moving-average (MA) model
Financial models	Binomial options pricing model Black–Derman–Toy Black–Karasinski Black–Scholes Chan–Karolyi–Longstaff–Sanders (CKLS) Chen Constant elasticity of variance (CEV) Cox–Ingersoll–Ross (CIR) Garman–Kohlhagen Heath–Jarrow–Morton (HJM) Heston Ho–Lee Hull–White Korn-Kreer-Lenssen LIBOR market Rendleman–Bartter SABR volatility Vašíček Wilkie
Actuarial models	Bühlmann Cramér–Lundberg Risk process Sparre–Anderson
Queueing models	Bulk Fluid Generalized queueing network M/G/1 M/M/1 M/M/c
Properties	Càdlàg paths Continuous Continuous paths Ergodic Exchangeable Feller-continuous Gauss–Markov Markov Mixing Piecewise-deterministic Predictable Progressively measurable Self-similar Stationary Time-reversible
Limit theorems	Central limit theorem Donsker's theorem Doob's martingale convergence theorems Ergodic theorem Fisher–Tippett–Gnedenko theorem Large deviation principle Law of large numbers (weak/strong) Law of the iterated logarithm Maximal ergodic theorem Sanov's theorem Zero–one laws (Blumenthal, Borel–Cantelli, Engelbert–Schmidt, Hewitt–Savage, Kolmogorov, Lévy)
Inequalities	Burkholder–Davis–Gundy Doob's martingale Doob's upcrossing Kunita–Watanabe Marcinkiewicz–Zygmund
Tools	Cameron–Martin formula Convergence of random variables Doléans-Dade exponential Doob decomposition theorem Doob–Meyer decomposition theorem Doob's optional stopping theorem Dynkin's formula Feynman–Kac formula Filtration Girsanov theorem Infinitesimal generator Itô integral Itô's lemma Karhunen–Loève theorem Kolmogorov continuity theorem Kolmogorov extension theorem Lévy–Prokhorov metric Malliavin calculus Martingale representation theorem Optional stopping theorem Prokhorov's theorem Quadratic variation Reflection principle Skorokhod integral Skorokhod's representation theorem Skorokhod space Snell envelope Stochastic differential equation Tanaka Stopping time Stratonovich integral Uniform integrability Usual hypotheses Wiener space Classical Abstract
Disciplines	Actuarial mathematics Control theory Econometrics Ergodic theory Extreme value theory (EVT) Large deviations theory Mathematical finance Mathematical statistics Probability theory Queueing theory Renewal theory Ruin theory Signal processing Statistics Stochastic analysis Time series analysis Machine learning
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