2019
DOI: 10.1016/j.jpdc.2019.03.013
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INRFlow: An interconnection networks research flow-level simulation framework

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Cited by 9 publications
(6 citation statements)
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References 22 publications
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“…This section describes phINRFlow (photonic Interconnection Network for Research Flow-level Simulation Framework), the simulation framework used to model the proposed approach, presenting the optical and network configurations used for the performed experiments, as well as the traffic patterns considered in these experiments. phINRFlow is a flow-level simulator for photonic in-terconnects that inherits functionality from INRFlow [35], originally developed with the aim of modeling electrical networks. It implement multiple, direct and indirect, network topologies (e.g.…”
Section: Methodsmentioning
confidence: 99%
“…This section describes phINRFlow (photonic Interconnection Network for Research Flow-level Simulation Framework), the simulation framework used to model the proposed approach, presenting the optical and network configurations used for the performed experiments, as well as the traffic patterns considered in these experiments. phINRFlow is a flow-level simulator for photonic in-terconnects that inherits functionality from INRFlow [35], originally developed with the aim of modeling electrical networks. It implement multiple, direct and indirect, network topologies (e.g.…”
Section: Methodsmentioning
confidence: 99%
“…These capabilities enable us to evaluate the system under realistic loads, giving us insight to its viability as a ToR switch. The simulator inherits functionality from INRFlow [22], wherein a detailed description of the simulator's methodology, organisation and workloads may be found. We model the ToR switch as a new topology, with unidirectional links and traffic flowing from łleftž to łrightž.…”
Section: Experimental Methodology 41 Simulator and Modelmentioning
confidence: 99%
“…The evaluation has been carried out using our in-house developed simulator INRFlow [29], which models the behaviour of large-scale parallel systems, including the network topology (link arrangement), the workload generation and the scheduling policies (selection, allocation and mapping) and measures several static (application-independent) and dynamic (with applications) properties. During execution, the links of the network have capacities and each flow is specified with a bandwidth reflecting the data that must be routed while also respecting the causal relationships between network flows -i.e, some flows must finish before others are allowed to be injected.…”
Section: Simulation Environment and Application Modelmentioning
confidence: 99%