Solving the steady-state number of customers in a queue

Steady-state solution to the G/G/C/N-like queue

G/G/C/N queue

To get started, select one of these examples, then press the "Submit" button:

Example 1 G/G/8/32 queue with with moderate load

Example 2 G/G/16/128 queue with regular service times

Example 3 G/G/64/256 queue with bursty arrivals

1) Type in the parameters of the queue
	C		Number of servers
	N		Maximum number in system (buffer + server)

2) Select the inter-arrival time
by: its mean and coefficient of variation or a specific Coxian distribution
	j t_a		Mean time between arrivals Number of phases (max=10)
	cv_a		Coefficient of variation

Arrivals follow a Coxian distribution with 2 stages with λ₁ = , λ₂ = and r_b1 = . change Use another Cox2 distribution by moving the cursor (or typing in a number if your browser is IE or FF) within the range 0.01; 0.9 min max Services follow a simple Exponential distribution with rate μ₁ = Services follow an Erlang or hypoexponential distribution with n = stages at rate μ₁ = and μ₂ = . Services follow a generalized Coxian distribution σ_i μ_i q_bi Phase 1: Phase 2: Phase 3: Phase 4: Phase 5: Phase 6: Phase 7: Phase 8: Phase 9: Phase 10:
3) Select the service time
by: its mean and coefficient of variation or a specific Coxian distribution
	k t_s		Mean service time Number of phases (max=10)
	cv_s		Coefficient of variation
Services follow a Coxian distribution with 2 stages with μ₁ = , μ₂ = and q_b1 = . change Use another Cox2 distribution by moving the cursor (or typing in a number if your browser is IE or FF) within the range 0.01; 0.9 min max Services follow a simple Exponential distribution with rate μ₁ = Services follow an Erlang or hypoexponential distribution with n = stages at rate μ₁ = and μ₂ = . Services follow a generalized Coxian distribution σ_i μ_i q_bi Phase 1: Phase 2: Phase 3: Phase 4: Phase 5: Phase 6: Phase 7: Phase 8: Phase 9: Phase 10:

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