Data center networking is the central infrastructure of the modern information society. However, benchmarking them is very challenging as the real-world network traffic is difficult to model, and Internet service giants treat the network traffic as confidential. Several industries have published a few publicly available network traces. However, these traces are collected from specific data center environments, e.g., applications, network topology, protocols, and hardware devices, and thus cannot be scaled to different users, underlying technologies, and varying benchmarking requirements. This article argues we should scale different data center applications and environments in designing, implementing, and evaluating data center networking benchmarking. We build DCNetBench, the first application-driven data center network benchmarking that can scale to different users, underlying technologies, and varying benchmarking requirements. The methodology is as follows. We built an emulated system that can simulate networking with different configurations. Then we run applications on the emulated systems to capture the realistic network traffic patterns; we analyze and classify these patterns to model and replay those traces. Finally, we provide an automatic benchmarking framework to support this pipeline. The evaluations on DCNetBench show its scaleability, effectiveness, and diversity for data center network benchmarking.

翻译：数据中心网络是现代信息社会的核心基础设施。然而，对其进行基准测试极具挑战性，因为真实网络流量难以建模，且互联网服务巨头将网络流量视为机密信息。行业已公开了少量网络轨迹数据集，但这些轨迹源于特定数据中心环境（如应用、网络拓扑、协议及硬件设备），无法扩展到不同用户、底层技术及多样化的基准测试需求。本文提出在数据中心网络基准测试的设计、实现与评估中，应扩展不同数据中心应用及环境。我们构建了DCNetBench——首个支持不同用户、底层技术及多样化基准测试需求的应用驱动型数据中心网络基准测试系统。其方法论如下：首先构建可模拟不同配置网络环境的仿真系统；随后在仿真系统上运行应用以捕获真实网络流量模式；通过分析与分类这些模式，实现流量轨迹的建模与回放；最终提供自动化基准测试框架以支撑上述流程。对DCNetBench的评估验证了其在数据中心网络基准测试中的可扩展性、有效性与多样性。