INRFlow: An interconnection networks research flow-level simulation framework
Date
2019
Authors
Navaridas, Javier
Pascual, Jose A.
Erickson, Alejandro
Stewart, Iain A.
Lujan, Mikel
Journal Title
Journal ISSN
Volume Title
Publisher
Journal of Parallel and Distributed Computing
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.
Description
Keywords
Simulation and modelling, Interconnection networks, Large-scale systems, Supercomputers, Datacentres, Network topologies and routing
Citation
Navaridas, J., Pascual, J.A., Erickson, A., Stewart, I.A. & Lujan, 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