M/G/k queue
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 and of the M/G/1 queue with a single server. Most performance metrics for this queueing system are not known and remain an open problem.[1]
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.
Steady state distribution
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] stationary distribution[4][5] and approximation by a reflected Brownian motion[6][7] have been offered by different authors
Delay/waiting time distribution
There are numerous approximations for the average delay a job experiences.[8][9][10][11][12][7] The first such was given in 1959 using a factor to adjust the mean waiting time in an M/M/c queue[13][14]
where C2 is the coefficient of variation of the service time distribution. Ward Whitt described this approximation as “usually an excellent approximation, even given extra information about the service-time distribution."[15]
However, it is known that no approximation using only the first two moments can be accurate in all cases.[13]
A Markov–Krein characterisation has been shown to produce tight bounds.[16]
Two servers
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[17] or the Laplace transform of the distribution when the service time distribution is a mixture of exponential distributions.[18] The Laplace transform of queue length[19] and waiting time distributions[20] can be computed when the waiting time distribution has a rational Laplace transform.
References
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