Averaging principles for a diffusion-scaled, heavy-traffic polling station with K job classes

Averaging principles for a diffusion-scaled, heavy-traffic polling station with K job classes

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Article ID: iaor20106297
Volume: 35
Issue: 3
Start Page Number: 669
End Page Number: 703
Publication Date: Aug 2010
Journal: Mathematics of Operations Research
Authors:
Keywords: polling systems
Abstract:

This paper provides heavy traffic limit theorems for a polling station serving jobs from K exogenous renewal arrival streams. It is a standard result that the limiting diffusion-scaled total workload process is semimartingale reflected Brownian motion. For polling stations, however, no such limit exists in general for the diffusion-scaled, K-dimensional queue length or workload vector processes. Instead, we prove that these processes admit averaging principles, the natures of which depend on the service discipline employed at the polling station. Parameterized families of exhaustive and gated service disciplines are investigated.

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