GapReplay: A High-Accuracy Packet Replayer

• Published in: ICC 2023 - IEEE International Conference on Communications pp. 1616 - 1621
• Main Authors: Yu, Shuanghong, Li, Fenghua, Zhang, Han, He, Kunling, Yao, Xingkun
• Format: Conference Proceeding
• Language: English
• Published: IEEE 28.05.2023
• Subjects: Hardware; Machine learning; Machine learning algorithms; Prediction algorithms; Production; Telecommunication traffic; Throughput
• ISSN: 1938-1883
• DOI: 10.1109/ICC45041.2023.10279258

Network traffic has become increasingly complicated and diverse with the continuing development of the Internet. It is challenging for the traffic generators to construct a large variety of traffic while maintaining the similarity to the real traffic. Given that real traffic from the production network is handy to retrieve, replaying traces from the production network can maximize the authenticity of traffic, contributing to experiments to evaluate the effectiveness of algorithms. Recently, many pieces of research in the networking field, especially algorithms based on machine learning (ML), are extremely sensitive to statistical characteristics of the traffic, such as ML-based traffic classification, traffic detection, traffic prediction, etc. However, there are only a few available statistics for the growing encrypted traffic, such as packet size and inter-arrival time (IAT). To replay traffic accurately, in this paper, we propose the GapReplay, a packet replayer that can remain identical with the original nanosecond-precision pcap trace in packet contents and achieve high accuracy in timestamps. GapReplay reaches line rate while transmitting packets by appending and extending packets, which will be dropped and truncated by a programmable device, to guarantee satisfying accuracy. We implement our mechanism on top of DPDK and P4. The evaluation results demonstrate that GapReplay can achieve a nanosecond-level accuracy, much better than the state-of-the-art such as MoonGen and tcpreplay, where the best of them is at least 1000 times less accurate than our mechanism.