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August 2004 Earliest-deadline-first service in heavy-traffic acyclic networks
Łukasz Kruk, John Lehoczky, Steven Shreve, Shu-Ngai Yeung
Ann. Appl. Probab. 14(3): 1306-1352 (August 2004). DOI: 10.1214/105051604000000314


This paper presents a heavy traffic analysis of the behavior of multi-class acyclic queueing networks in which the customers have deadlines. We assume the queueing system consists of J stations, and there are K different customer classes. Customers from each class arrive to the network according to independent renewal processes. The customers from each class are assigned a random deadline drawn from a deadline distribution associated with that class and they move from station to station according to a fixed acyclic route. The customers at a given node are processed according to the earliest-deadline-first (EDF) queue discipline. At any time, the customers of each type at each node have a lead time, the time until their deadline lapses. We model these lead times as a random counting measure on the real line. Under heavy traffic conditions and suitable scaling, it is proved that the measure-valued lead-time process converges to a deterministic function of the workload process. A two-station example is worked out in detail, and simulation results are presented to illustrate the predictive value of the theory. This work is a generalization of Doytchinov, Lehoczky and Shreve [Ann. Appl. Probab. 11 (2001) 332–379], which developed these results for the single queue case.


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Łukasz Kruk. John Lehoczky. Steven Shreve. Shu-Ngai Yeung. "Earliest-deadline-first service in heavy-traffic acyclic networks." Ann. Appl. Probab. 14 (3) 1306 - 1352, August 2004.


Published: August 2004
First available in Project Euclid: 13 July 2004

zbMATH: 1084.60056
MathSciNet: MR2071425
Digital Object Identifier: 10.1214/105051604000000314

Primary: 60K25
Secondary: 60G57 , 60J65 , 68M20

Keywords: Acyclic networks , diffusion limits , Due dates , heavy traffic , Queueing , random measures.

Rights: Copyright © 2004 Institute of Mathematical Statistics


Vol.14 • No. 3 • August 2004
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