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  2. M/M/c queue - Wikipedia

    en.wikipedia.org/wiki/M/M/c_queue

    In queueing theory, a discipline within the mathematical theory of probability, the M/M/c queue (or Erlang–C model [1]: 495 ) is a multi-server queueing model. [2] In Kendall's notation it describes a system where arrivals form a single queue and are governed by a Poisson process, there are c servers, and job service times are exponentially distributed. [3]

  3. M/M/∞ queue - Wikipedia

    en.wikipedia.org/wiki/M/M/%E2%88%9E_queue

    An M/M/∞ queue is a stochastic process whose state space is the set {0,1,2,3,...} where the value corresponds to the number of customers currently being served. Since, the number of servers in parallel is infinite, there is no queue and the number of customers in the systems coincides with the number of customers being served at any moment.

  4. Queueing theory - Wikipedia

    en.wikipedia.org/wiki/Queueing_theory

    In 1953, David George Kendall solved the GI/M/k queue [15] and introduced the modern notation for queues, now known as Kendall's notation. In 1957, Pollaczek studied the GI/G/1 using an integral equation. [16] John Kingman gave a formula for the mean waiting time in a G/G/1 queue, now known as Kingman's formula. [17]

  5. Burke's theorem - Wikipedia

    en.wikipedia.org/wiki/Burke's_theorem

    The theorem can be generalised for "only a few cases," but remains valid for M/M/c queues and Geom/Geom/1 queues. [7]It is thought that Burke's theorem does not extend to queues fed by a Markovian arrival processes (MAP) and is conjectured that the output process of an MAP/M/1 queue is an MAP only if the queue is an M/M/1 queue.

  6. Kingman's formula - Wikipedia

    en.wikipedia.org/wiki/Kingman's_formula

    Kingman's approximation states: () (+)where () is the mean waiting time, τ is the mean service time (i.e. μ = 1/τ is the service rate), λ is the mean arrival rate, ρ = λ/μ is the utilization, c a is the coefficient of variation for arrivals (that is the standard deviation of arrival times divided by the mean arrival time) and c s is the coefficient of variation for service times.

  7. Kendall's notation - Wikipedia

    en.wikipedia.org/wiki/Kendall's_notation

    M: Markovian or memoryless [6] Exponential service time. M/M/1 queue: M Y: bulk Markov: Exponential service time with a random variable Y for the size of the batch of entities serviced at one time. M X /M Y /1 queue: D: Degenerate distribution: A deterministic or fixed service time. M/D/1 queue: E k: Erlang distribution