Asymptotic Optimality of Speed-Aware JSQ for Heterogeneous Systems

TL;DR

This paper proves that a speed-aware join-the-shortest-queue scheme achieves delay optimality in large heterogeneous server systems by developing new techniques for establishing tightness and analyzing the fluid limit.

Contribution

It introduces a novel speed-aware JSQ scheme for heterogeneous systems and demonstrates its asymptotic delay optimality using innovative Lyapunov drift techniques.

Findings

Achieves delay optimality in the fluid limit for heterogeneous systems.

Develops a new technique using Lyapunov drift for tightness in steady-state distributions.

Shows the fluid limit has a unique, globally attractive fixed point.

Abstract

The Join-the-Shortest-Queue (JSQ) load-balancing scheme is known to minimise the average delay of jobs in homogeneous systems consisting of identical servers. However, it performs poorly in heterogeneous systems where servers have different processing rates. Finding a delay optimal scheme remains an open problem for heterogeneous systems. In this paper, we consider a speed-aware version of the JSQ scheme for heterogeneous systems and show that it achieves delay optimality in the fluid limit. One of the key issues in establishing this optimality result for heterogeneous systems is to show that the sequence of steady-state distributions indexed by the system size is tight in an appropriately defined space. The usual technique for showing tightness by coupling with a suitably defined dominant system does not work for heterogeneous systems. To prove tightness, we devise a new technique that uses the drift of exponential Lyapunov functions. Using the non-negativity of the drift, we show that the stationary queue length distribution has an exponentially decaying tail - a fact we use to prove tightness. Another technical difficulty arises due to the complexity of the underlying state-space and the separation of two time-scales in the fluid limit. Due to these factors, the fluid-limit turns out to be a function of the invariant distribution of a multi-dimensional Markov chain which is hard to characterise. By using some properties of this invariant distribution and using the monotonicity of the system, we show that the fluid limit is has a unique and globally attractive fixed point.