INRFlow: An interconnection networks research flow-level simulation framework

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

doi:10.1016/j.jpdc.2019.03.013

INRFlow: An interconnection networks research flow-level simulation framework

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

Authors

Javier Navaridas

Jose A. Pascual

Alejandro Erickson

Professor Iain Stewart i.a.stewart@durham.ac.uk
Professor

Mikel Luján

Abstract

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.

Citation

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. https://doi.org/10.1016/j.jpdc.2019.03.013

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
DOI	https://doi.org/10.1016/j.jpdc.2019.03.013

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Copyright Statement
© 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/)