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G/G/∞ queues with renewal alternating interruptions

Published online by Cambridge University Press:  19 September 2016

Guodong Pang*
Affiliation:
Pennsylvania State University
Yuhang Zhou*
Affiliation:
Pennsylvania State University
*
* Postal address: The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, PA 16802, USA.
* Postal address: The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, PA 16802, USA.
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Abstract

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We study G/G/∞ queues with renewal alternating service interruptions, where the service station experiences `up' and `down' periods. The system operates normally in the up periods, and all servers stop functioning while customers continue entering the system during the down periods. The amount of service a customer has received when an interruption occurs will be conserved and the service will resume when the down period ends. We use a two-parameter process to describe the system dynamics: X r (t,y) tracking the number of customers in the system at time t that have residual service times strictly greater than y. The service times are assumed to satisfy either of the two conditions: they are independent and identically distributed with a distribution of a finite support, or are a stationary and weakly dependent sequence satisfying the ϕ-mixing condition and having a continuous marginal distribution function. We consider the system in a heavy-traffic asymptotic regime where the arrival rate gets large and service time distribution is fixed, and the interruption down times are asymptotically negligible while the up times are of the same order as the service times. We show the functional law of large numbers and functional central limit theorem (FCLT) for the process X r (t,y) in this regime, where the convergence is in the space 𝔻([0,∞), (𝔻, L 1)) endowed with the Skorokhod M 1 topology. The limit processes in the FCLT possess a stochastic decomposition property.

Information

Type
Research Article
Copyright
Copyright © Applied Probability Trust 2016