Benchmarking is commonly used in research fields, such as computer architecture design and machine learning, as a powerful paradigm for rigorously assessing, comparing, and developing novel technologies. However, the data centre network (DCN) community lacks a standard open-access and reproducible traffic generation framework for benchmark workload generation. Driving factors behind this include the proprietary nature of traffic traces, the limited detail and quantity of open-access network-level data sets, the high cost of real world experimentation, and the poor reproducibility and fidelity of synthetically generated traffic. This is curtailing the community's understanding of existing systems and hindering the ability with which novel technologies, such as optical DCNs, can be developed, compared, and tested.

We present TrafPy; an open-access framework for generating both realistic and custom DCN traffic traces. TrafPy is compatible with any simulation, emulation, or experimentation environment, and can be used for standardised benchmarking and for investigating the properties and limitations of network systems such as schedulers, switches, routers, and resource managers. We give an overview of the TrafPy traffic generation framework, and provide a brief demonstration of its efficacy through an investigation into the sensitivity of some canonical scheduling algorithms to varying traffic trace characteristics in the context of optical DCNs. TrafPy is open-sourced via GitHub and all data associated with this manuscript via RDR.

基准测试通常用于研究领域，例如计算机架构设计和机器学习，作为严格评估、比较和开发新技术的强大范式。然而，数据中心网络 (DCN) 社区缺乏用于基准工作负载生成的标准开放访问和可重复的流量生成框架。这背后的驱动因素包括流量跟踪的专有性质、开放访问网络级数据集的有限细节和数量、现实世界实验的高成本以及合成生成的流量的可重复性和保真度差。这削弱了社区对现有系统的理解，并阻碍了开发、比较和测试光学 DCN 等新技术的能力。

我们介绍 TrafPy；用于生成真实和自定义 DCN 流量跟踪的开放访问框架。TrafPy 与任何模拟、仿真或实验环境兼容，可用于标准化基准测试以及调查调度程序、交换机、路由器和资源管理器等网络系统的属性和限制。我们概述了 TrafPy 流量生成框架，并通过调查一些规范调度算法对光 DCN 环境中不同流量跟踪特征的敏感性来简要展示其功效。TrafPy 是通过 GitHub 开源的，与本手稿相关的所有数据都通过 RDR 开源。