Information-Centric Networking (ICN) has gained attention as one of the next-generation internet architectures that focuses on the data being transmitted rather than the hosts transmitting it. Due to the differences between ICN and TCP/IP networks, it is not possible to evaluate the performance of ICN using network simulators designed for TCP/IP. A number of studies have been conducted to develop ICN network simulators. However, further acceleration of ICN network simulators is expected to enable large-scale ICN network performance evaluation. In this paper, we analyze the performance bottleneck of the flow-level ICN simulator called FICNSIM (Fluid-based ICNSIMulator) by profiling its performance using the Julia language source code. Specifically, we identify the processing that is causing the performance bottleneck of FICNSIM and investigate the scalability of FICNSIM with respect to network scale.

In recent years, ICN (Information-Centric Networking) that focuses on the data being transferred, rather than hosts exchanging the data, has been attracting attention as one of the promising next-generation Internet architectures. It has developed that fluid model of large-scale ICN networks, which is aimed at analyzing the performance of transport layer protocols in ICN networks. In this paper, we present a flow-level ICN sim-ulator called FICNSIM (Fluid-based ICN SIMulator), which is based on the numerical solver for ICN fluid models. In particular, we introduce two types of FICNSIM implementations: a highly customizable implementation in the Python language and a high-performance implementation in the Julia language. Furthermore, through several experiments, we evaluate the effectiveness of our FICNSIM implementation. Consequently, we show that our implemented FICNSIM can perform a high-speed simulation execution compared to a conventional packet-level ICN simulator.