Skip to main content

Research Repository

Advanced Search

INRFlow: An interconnection networks research flow-level simulation framework

Navaridas, Javier; Pascual, Jose A.; Erickson, Alejandro; Stewart, Iain A.; Luján, Mikel

INRFlow: An interconnection networks research flow-level simulation framework Thumbnail


Javier Navaridas

Jose A. Pascual

Alejandro Erickson

Mikel Luján


This paper presents INRFlow, a mature, frugal, flow-level simulation framework for modelling large-scale networks and computing systems. INRFlow is designed to carry out performance-related studies of interconnection networks for both high performance computing systems and datacentres. It features a completely modular design in which adding new topologies, routings or traffic models requires minimum effort. Moreover, INRFlow includes two different simulation engines: a static engine that is able to scale to tens of millions of nodes and a dynamic one that captures temporal and causal relationships to provide more realistic simulations. We will describe the main aspects of the simulator, including system models, traffic models and the large variety of topologies and routings implemented so far. We conclude the paper with a case study that analyses the scalability of several typical topologies. INRFlow has been used to conduct a variety of studies including evaluation of novel topologies and routings (both in the context of graph theory and optimization), analysis of storage and bandwidth allocation strategies and understanding of interferences between application and storage traffic.


Navaridas, J., Pascual, J. A., Erickson, A., Stewart, I. A., & Luján, M. (2019). INRFlow: An interconnection networks research flow-level simulation framework. Journal of Parallel and Distributed Computing, 130, 140-152.

Journal Article Type Article
Acceptance Date Mar 20, 2019
Online Publication Date Mar 28, 2019
Publication Date Aug 31, 2019
Deposit Date Apr 1, 2019
Publicly Available Date Apr 23, 2019
Journal Journal of Parallel and Distributed Computing
Print ISSN 0743-7315
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 130
Pages 140-152


You might also like

Downloadable Citations