INRFlow: An interconnection networks research flow-level simulation framework

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

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
• Electronic ISSN: 1096-0848
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 130
• Pages: 140-152
• DOI: https://doi.org/10.1016/j.jpdc.2019.03.013
• Public URL: https://durham-repository.worktribe.com/output/1334096