INRFlow: An Interconnection Networks Research Flow-level Simulation Framework

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

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
JournalJournal of Parallel and Distributed Computing
Early online date29 Mar 2019
Publication statusPublished - 2019


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