Invariant states of hydrodynamic limits of randomized load balancing networks

TL;DR

This paper characterizes invariant states of hydrodynamic equations for load balancing networks, showing exponential tail decay and providing numerical methods, thus advancing understanding of large-scale queueing systems.

Contribution

It offers a new characterization of invariant states for hydrodynamic equations in load balancing networks and develops a numerical algorithm for key performance metrics.

Findings

Invariant states exhibit doubly exponential tail decay under certain conditions.

Numerical algorithms for queue length distribution and waiting time are developed.

Evidence suggests invariant states are limits of steady-state distributions.

Abstract

Randomized load-balancing algorithms play an important role in improving performance in large-scale networks at relatively low computational cost. A common model of such a system is a network of $N$ parallel queues in which incoming jobs with independent and identically distributed service times are routed on arrival using the join-the-shortest-of-$d$-queues routing algorithm. Under fairly general conditions, it was shown by Aghajani and Ramanan that as $N\rightarrow\infty$, the state dynamics converges to the unique solution of a countable system of coupled deterministic measure-valued equations called the hydrodynamic equations. In this article, a characterization of invariant states of these hydrodynamic equations is obtained and, when $d=2$, used to construct a numerical algorithm to compute the queue length distribution and mean virtual waiting time in the invariant state. Additionally, it is also shown that under a suitable tail condition on the service distribution, the queue length distribution of the invariant state exhibits a doubly exponential tail decay, thus demonstrating a vast improvement in performance over the case $d=1$, which corresponds to random routing, when the tail decay could even be polynomial. Furthermore, numerical evidence is provided to support the conjecture that the invariant state is the limit of the steady-state distributions of the $N$-server models. The proof methodology, which entails analysis of a coupled system of measure-valued equations, can potentially be applied to other many-server systems with general service distributions, where measure-valued representations are useful.