On the Control of Fork-Join Networks

TL;DR

This paper addresses scheduling in fork-join networks with shared resources, proposing optimal rules under heavy traffic conditions and demonstrating asymptotic optimality for such complex, synchronized processing systems.

Contribution

It introduces a novel scheduling approach for fork-join networks with shared resources and proves its asymptotic optimality in heavy traffic scenarios.

Findings

Proposed a scheduling rule for shared resource in fork-join networks.

Proved asymptotic optimality of the scheduling rule under heavy traffic.

Enhanced understanding of synchronization constraints in complex networks.

Abstract

Networks in which the processing of jobs occurs both sequentially and in parallel are prevalent in many application domains, such as computer systems, healthcare, manufacturing, and project management. The parallel processing of jobs gives rise to synchronization constraints that can be a main reason for job delay. In comparison with feedforward queueing networks that have only sequential processing of jobs, the approximation and control of networks that have synchronization constraints is less understood. One well-known modeling framework in which synchronization constraints are prominent is the fork-join processing network. Our objective is to find scheduling rules for fork-join processing networks with multiple job types in which there is first a fork operation, then activities that can be performed in parallel, and then a join operation. The difficulty is that some of the activities that can be performed in parallel require a shared resource. We solve the scheduling problem for that shared server (that is, which type of job to prioritize each time the server becomes available) when that server is in heavy traffic and prove an asymptotic optimality result.