Research Article
A New End-To-End Network Traffic Reconstruction Approach Based on Different Time Granularities
@INPROCEEDINGS{10.1007/978-3-030-97124-3_10, author={Wei Yang and Dingde Jiang and Jianguang Chen and Zhihao Wang and Liuwei Huo and Wenhui Zhao}, title={A New End-To-End Network Traffic Reconstruction Approach Based on Different Time Granularities}, proceedings={Simulation Tools and Techniques. 13th EAI International Conference, SIMUtools 2021, Virtual Event, November 5-6, 2021, Proceedings}, proceedings_a={SIMUTOOLS}, year={2022}, month={3}, keywords={End-to-end network traffic reconstruction Fractal interpolation Compression sense Dictionary Learning Algorithm}, doi={10.1007/978-3-030-97124-3_10} }- Wei Yang
Dingde Jiang
Jianguang Chen
Zhihao Wang
Liuwei Huo
Wenhui Zhao
Year: 2022
A New End-To-End Network Traffic Reconstruction Approach Based on Different Time Granularities
SIMUTOOLS
Springer
DOI: 10.1007/978-3-030-97124-3_10
Abstract
End-to-end network traffic is an important input parameter for network planning and network monitoring, which plays an important role in network management and design. This paper proposes a new end-to-end network traffic reconstruction algorithm based on different time granularity. This algorithm reconstructs the end-to-end network traffic with fine time granularity by taking advantage of the characteristics of the link traffic which is easy to be measured directly in the network with coarse time granularity. According to the fractal and self-similar characteristics of network traffic found in existing studies, we first carry out fractal interpolation for link traffic measurement under coarse time granularity to obtain link traffic under fine time granularity. Then, by using the compressive sensing theory, an appropriate sparse transformation matrix and measurement matrix are constructed to reconstruct the end-to-end network traffic with fine time granularity. Simulation results show that the proposed algorithm is effective and feasible.
