DCNetBench: Scaleable Data Center Network Benchmarking
Ke Liu, Wanling Gao, Chunjie Luo, Cheng Huang, Chunxin Lan, Zhenxing, Zhang, Lei Wang, Xiwen He, Nan Li, and Jianfeng Zhan

TL;DR
DCNetBench is a scalable, application-driven benchmarking framework for data center networks that captures, models, and replays realistic traffic patterns across diverse environments and technologies.
Contribution
It introduces the first scalable, application-driven data center network benchmarking framework that models and replays traffic for varied environments and technologies.
Findings
Demonstrates scalability and effectiveness of DCNetBench.
Shows diversity in traffic patterns captured.
Validates applicability across different data center setups.
Abstract
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…
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Taxonomy
TopicsCloud Computing and Resource Management · Software-Defined Networks and 5G · Caching and Content Delivery
