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Finite-time Convergence to Stationarity of M/M/n queues: a Lyapunov-Poincaré Approach

 

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Model

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Motivations

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M/M/n behavior

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Heavy-traffic parameter

 

M/M/1

 

 

 

 

 

 

 

 

 

 

 

M/M/n

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M/M/n behavior

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Phase transition

Halfin, Whitt (1981)

 

Heavy-traffic parameter

Mean field

Heavy Traffic

Super-HW

Sub-HW

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Related works: Finite-time

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Gamarnik , Goldberg (’13)

Spectral method

 

 

 

 

 

 

Roberts (2003)

Hitting time

Zeifman (1991)

Log-norm

Chafai (2006)

Binomial-Poisson

 

 

Can we get tight convergence bounds for all regimes using a unifying method?

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Main results: All regimes (NVM 2025)

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Initial distribution

stationary distribution

 

First convergence to stationarity result for queuing systems in Chi-square distance!

M/M/1

 

NVM 2025, Zeifman

NVM 2025

NVM 2025

Heavy Traffic

Mean field

- Moment bound

- Tail bound

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Proof Framework

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Proof framework

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Approach: Lyapunov drift

 

 

Generator

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Proof framework

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Step 1: Lyapunov drift

Step 2: Local mixing

 

Step 3:

 

 

Stitching error

 

 

Minorization: Rosenthal et al. (1995)

Atom set: Meyn et al. (1996)

Canonical path method: NVM (2025)

Truncation method: NVM (2025)

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Proof framework

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Step 1: Lyapunov drift

Step 2: Local mixing

 

 

Step 3:

 

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Step 1: Lyapunov drift

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Strong

Two-sided drift!

 

 

K

 

 

 

 

 

M/M/1

 

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Proof framework

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Step 1: Lyapunov drift

Step 2: Local mixing

 

 

 

Step 3:

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Step 2: Local mixing

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K

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Canonical path method

 

 

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Super-HW regime proof sketch

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Step 1: Lyapunov drift

Step 2: Local mixing

 

Step 3:

 

 

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Summary

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M/M/1

Unifying Lyapunov-Poincaré method!

Applicable to reversible Markov chains

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Takeaway

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Finite state space

Countable

- Coupling/Coupling from the past

- Conductance

- Spectral Independence

- Evolving sets

- Wilson’s method

- Isoperimetry inequalities

- Eigenvalue method

Lyapunov drift method

Stitching Theorem

Coupling/hitting time method

Log-norm

Spectral method

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Thank you for listening!

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Check out our paper here: