Heavy-traffic waiting-time distribution conjecture for roving server networks

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Let WiW_i be the waiting time of a type-ii customer, and let Wifluid\mathcal{W}^{\textit{fluid}}_i be the standardised waiting time of a type-ii particle in the fluid model. Let Γ\Gamma be independent of Wifluid\mathcal{W}^{\textit{fluid}}_i, with Gamma parameters α+1\alpha+1 and δμ\delta\mu. Waiting-time conjecture. As ρ1\rho\uparrow1, the scaled waiting time (1ρ)Wi(1-\rho)W_i converges in distribution to the product of these two independent random variables:

(1ρ)WidΓ×Wifluid.(1-\rho)W_i\xrightarrow{d}\Gamma\times\mathcal{W}^{\textit{fluid}}_i.

The assertion applies for i=1,,Ni=1,\dots,N and k=iN,,i1k=i-N,\dots,i-1. It follows the heavy-traffic averaging principle and is not proved in the supplied text for general arrivals.

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Primary source

Marko Boon, Rob van der Mei and Erik Winands, “Heavy traffic analysis of roving server networks”, arXiv:1611.02608 (2016).

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