Accelerated traffic replay method based on time compression

Shuo WANG; Jun BAI; Bailing WANG; Xu ZHANG; Hongri LIU

doi:10.11959/j.issn.2096-109x.2021082

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Accelerated traffic replay method based on time compression

Papers | 更新时间：2024-06-03

- Accelerated traffic replay method based on time compression
- Chinese Journal of Network and Information Security Vol. 7, Issue 5, Pages: 178-188(2021)
- 作者机构：
- 作者简介：
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- DOI：10.11959/j.issn.2096-109x.2021082
  CLC： TP393
- Online First：2021-10，
  
  Published：15 October 2021
- 稿件说明：
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Shuo WANG, Jun BAI, Bailing WANG, et al. Accelerated traffic replay method based on time compression[J]. Chinese Journal of Network and Information Security, 2021, 7(5): 178-188.
DOI：

Shuo WANG, Jun BAI, Bailing WANG, et al. Accelerated traffic replay method based on time compression[J]. Chinese Journal of Network and Information Security, 2021, 7(5): 178-188. DOI： 10.11959/j.issn.2096-109x.2021082.

摘要

随着网络流量的激增，在虚拟环境下对流量进行回放面临着许多问题。为了满足回放过程中流量完整性和准确性的需求，提出了一种基于时间压缩的流量加速回放方法，实现了在虚拟环境下对真实网络流量的加速回放。该方法优先将时间间隔集合中最大的时间间隔进行压缩，对间隔较小的时间减少压缩，从而减少时间压缩造成的单位时间流量猛增现象，进而降低流量的丢包率。从丢包率、时间间隔误差、相似度3方面对其效果进行衡量。实验表明，与等比压缩方法相比，该方法在相似度上表现较差，但有着较低的丢包率和时间间隔误差。

Abstract

With the proliferation of network traffic

replaying traffic in a virtual environment faces many problems.In order to meet the requirements of traffic integrity and accuracy in the replay process

an accelerated traffic replay method based on time-compressed was proposed to achieve accelerated replay of real network traffic in a virtual environment.The method prioritized the compression of the largest time interval in the set of time intervals

and reduced the compression of the smaller intervals

thus reducing the traffic spike per unit time caused by time compression

and reducing the packet loss rate of traffic.The effect was measured in terms of packet loss rate

time interval error and similarity.The experiments show that this method has a lower packet loss rate and lower time interval error compared with the isometric compression method

although it performs worse in similarity.

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