Pull-based load distribution among heterogeneous parallel servers: the case of multiple routers

TL;DR

This paper analyzes a multi-router pull-based load balancing algorithm in large heterogeneous server pools, proving its asymptotic optimality and low communication overhead under certain conditions.

Contribution

It extends the PULL algorithm to multiple routers and proves its asymptotic optimality in large-scale systems with minimal message exchange.

Findings

Asymptotic optimality of PULL in multi-router systems

Vanishing customer blocking/waiting probability as system scales

Low message exchange rate of one message per customer

Abstract

The model is a service system, consisting of several large server pools. A server processing speed and buffer size (which may be finite or infinite) depend on the pool. The input flow of customers is split equally among a fixed number of routers, which must assign customers to the servers immediately upon arrival. We consider an asymptotic regime in which the customer total arrival rate and pool sizes scale to infinity simultaneously, in proportion to a scaling parameter $n$, while the number of routers remains fixed. We define and study a multi-router generalization of the pull-based customer assignment (routing) algorithm PULL, introduced in [11] for the single-router model. Under PULL algorithm, when a server becomes idle it send a "pull-message" to a randomly uniformly selected router; each router operates independently -- it assigns an arriving customer to a server according to a randomly uniformly chosen available (at this router) pull-message, if there is any, or to a randomly uniformly selected server in the entire system, otherwise. Under Markov assumptions (Poisson arrival process and independent exponentially distributed service requirements), and under sub-critical system load, we prove asymptotic optimality of PULL: as $n\to\infty$, the steady-state probability of an arriving customer experiencing blocking or waiting, vanishes. Furthermore, PULL has an extremely low router-server message exchange rate of one message per customer. These results generalize some of the single-router results in [11].