In queueing theory, a discipline within the mathematical theory of probability, a heavy traffic approximation (sometimes heavy traffic limit theorem or diffusion approximation) is the matching of a queueing model with a diffusion process under some limiting conditions on the model's parameters. The first such result was published by John Kingman who showed that when the utilisation parameter of an M/M/1 queue is near 1 a scaled version of the queue length process can be accurately approximated by a reflected Brownian motion.
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Heavy traffic condition
Heavy traffic approximations are typically stated for the process X(t) describing the number of customers in the system at time t. They are arrived at by considering the model under the limiting values of some model parameters and therefore for the result to be finite the model must be rescaled by a factor n, denoted
and the limit of this process is considered as n → ∞.
There are three classes of regime under which such approximations are generally considered.
- The number of servers is fixed and the traffic intensity (utilization) is increased to 1 (from below). The queue length approximation is a reflected Brownian motion.
- Traffic intensity is fixed and the number of servers and arrival rate are increased to infinity. Here the queue length limit converges to the normal distribution.
- A quantity β is fixed where
Results for a G/G/1 queue
Theorem 1. Consider a sequence of G/G/1 queues indexed by
For queue
let
Suppose that
provided that:
(a)
Heuristic argument
Let
Then by definition:
After recursive calculation, we have:
Let
Then we have
we get
Thus the waiting time in queue of the nth customer
Random walk can be approximated by a Brownian motion when the jump sizes approach 0 and the times between the jump approach 0.
We have
Theorem 2. Let
if
otherwise
Conclusion
Thus, the heavy traffic limit theorem (Theorem 1) is heuristically argued. Formal proofs usually follow a different approach which involve characteristic functions.
Example
Consider an M/G/1 queue with arrival rate
The exact average waiting time in queue in steady state is give by:
The corresponding heavy traffic approximation:
The relative error of the heavy traffic approximation:
Thus when