Tail Asymptotics for the $M_1,M_2/G_1,G_2/1$ Retrial Queue with Priority

Abstract

Stochastic networks with complex structures are key modelling tools for many important applications. In this paper, we consider a specific type of network: the retrial queueing systems with priority. This type of queueing system is important in various applications, including telecommunication and computer management networks with big data. For this type of system, we propose a detailed stochastic decomposition approach to study its asymptotic behaviour of the tail probability of the number of customers in the steady-state for retrial queues with two types (Type-1 and Type-2) of customers, in which Type-1 customers (in a queue) have non-preemptive priority to receive service over Type-2 customers (in an orbit). Under the assumption that the service times of Type-1 customers have a regularly varying tail and the service times of Type-2 customers have a tail lighter than Type-1 customers, we obtain tail asymptotic properties for the number of customers in the queue and in the orbit, respectively, conditional on the server's status, in terms of a detailed stochastic decomposition approach. Tail asymptotic properties are often used as key tools for approximating various performance metrics and constructing numerical algorithms for computations.

arXiv comment25 pages

References in corpus (5)

• Retrial Queueing Models: A Survey on Theory and Applications
• Refined Tail Asymptotic Properties for the $M^X/G/1$ Retrial Queue
• Asymptotics of Queue Length Distributions in Priority Retrial Queues
• Second Order Asymptotic Properties for the Tail Probability of the Number of Customers in the M/G/1 Retrial Queue
• Tail Asymptotics for a Retrial Queue with Bernoulli Schedule