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Bayesian Estimation of M/Gumbel/1 Queueing Model for Heavy Tailed Congestion

K. Senthamarai Kannan, A. Jabarali, A. Jawahar Farook


The general assumptions of the inter-arrival and service times in queue are disappeared, when unusual characteristics such as self-similarity, long-range dependence, burstiness and heavy tails [1]. Here, it is considered the single server queue in which service time follows heavy tailed distribution partic- ularly, Gumbel distribution. This paper exhibits the estimation of the traffic intensity of above mentioned queueing model with satisfying the stability condition using Bayesian approach through Gibbs sampling algorithm based on informative prior.

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