Design Heuristic for Parallel Many Server Systems under FCFS-ALIS

TL;DR

This paper introduces a heuristic for analyzing large parallel many-server systems under FCFS-ALIS, using bipartite infinite matching models to approximate matching rates and inform system design.

Contribution

It proposes a heuristic approach to approximate matching rates in complex systems using bipartite infinite matching models, supported by simulation evidence.

Findings

Matching rates are well approximated by bipartite infinite matching models in large systems.

Simulation results support the heuristic's accuracy for system design.

The approach enables designing systems to meet specific performance criteria.

Abstract

We study a parallel queueing system with multiple types of servers and customers. A bipartite graph describes which pairs of customer-server types are compatible. We consider the service policy that always assigns servers to the first, longest waiting compatible customer, and that always assigns customers to the longest idle compatible server if on arrival, multiple compatible servers are available. For a general renewal stream of arriving customers and general service time distributions, the behavior of such systems is very complicated. In particular, the calculation of matching rates, the fraction of services of customer-server type, is intractable. We suggest through a heuristic argument that if the number of servers becomes large, the matching rates are well approximated by matching rates calculated from the tractable bipartite infinite matching model. We present simulation evidence to support this heuristic argument, and show how this can be used to design systems with desired performance requirements.