Steady-state analysis of load-balancing algorithms in the sub-Halfin–Whitt regime

Author:

Liu Xin,Ying Lei

Abstract

AbstractWe study a class of load-balancing algorithms for many-server systems (N servers). Each server has a buffer of size $b-1$ with $b=O(\sqrt{\log N})$, i.e. a server can have at most one job in service and $b-1$ jobs queued. We focus on the steady-state performance of load-balancing algorithms in the heavy traffic regime such that the load of the system is $\lambda = 1 - \gamma N^{-\alpha}$ for $0<\alpha<0.5$ and $\gamma > 0,$ which we call the sub-Halfin–Whitt regime ($\alpha=0.5$ is the so-called Halfin–Whitt regime). We establish a sufficient condition under which the probability that an incoming job is routed to an idle server is 1 asymptotically (as $N \to \infty$) at steady state. The class of load-balancing algorithms that satisfy the condition includes join-the-shortest-queue, idle-one-first, join-the-idle-queue, and power-of-d-choices with $d\geq \frac{r}{\gamma}N^\alpha\log N$ (r a positive integer). The proof of the main result is based on the framework of Stein’s method. A key contribution is to use a simple generator approximation based on state space collapse.

Publisher

Cambridge University Press (CUP)

Subject

Statistics, Probability and Uncertainty,General Mathematics,Statistics and Probability

Cited by 24 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Large-System Insensitivity of Zero-Waiting Load Balancing Algorithms;Stochastic Systems;2024-01-22

2. Power-of-d Choices Load Balancing in the Sub-Halfin Whitt Regime;ACM SIGMETRICS Performance Evaluation Review;2023-06-26

3. Power-of-d Choices Load Balancing in the Sub-Halfin Whitt Regime;Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems;2023-06-19

4. Diffusion-level universality of many-server systems with concurrent service;Operations Research Letters;2023-05

5. The Join-the-Shortest-Queue System in the Halfin-Whitt Regime: Rates of Convergence to the Diffusion Limit;Stochastic Systems;2023-03

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