Heavy-Traffic Limits for a Many-Server Queueing Network with Switchover

DOI10.1239/aap/1377868533zbMath1282.60091OpenAlexW2126173409MaRDI QIDQ2856030

David D. W. Yao, Guodong Pang

Publication date: 23 October 2013

Published in: Advances in Applied Probability (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.aap/1377868533

zbMATH Keywords

heavy trafficcustomer abandonmentdiffusion limitfluid limitmulticlassQED regimeED regimemany server queueMarkovian networkmixed regimemultidimensional (piecewise linear) Ornstein-Uhlenbeck processstate-dependent switchovertime-varying arrival

Mathematics Subject Classification ID

Queueing theory (aspects of probability theory) (60K25) Queues and service in operations research (90B22) Functional limit theorems; invariance principles (60F17)

Related Items

Optimal control of a multiclass queueing system when customers can change types, Infinite horizon asymptotic average optimality for large-scale parallel server networks

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Positive recurrence of piecewise Ornstein-Uhlenbeck processes and common quadratic Lyapunov functions
• Fluid limits of many-server queues with reneging
• Martingale proofs of many-server heavy-traffic limits for Markovian queues
• Heavy-traffic limits for waiting times in many-server queues with abandonment
• Strong approximation for Markovian service networks
• Spatial homogenization in a stochastic network with mobility
• Flow-level performance and capacity of wireless networks with user mobility
• Stochastic-Process Limits
• Efficiency-Driven Heavy-Traffic Approximations for Many-Server Queues with Abandonments
• Queue-and-Idleness-Ratio Controls in Many-Server Service Systems
• Dimensioning Large Call Centers
• Heavy-Traffic Limits for Queues with Many Exponential Servers
• On the Invariance Principle for the First Passage Time
• Many-server queues with customer abandonment: numerical analysis of their diffusion models
• A Fluid Limit for an Overloaded X Model via a Stochastic Averaging Principle