Datacenter Network Deserves Better Traffic Models
Authors: 
Sijiang Huang, Lingfeng Peng, Mowei Wang, Yashe Liu, Zhenhua Liu, Xin Wang, Yong CuiAuthors Info & Claims
Pages 124 - 130
Abstract
Traffic modeling of Datacenter Network (DCN) today is over-simplified, deviating from the ground truth. Adopted by numerous researchers, the common practice relies on the assumptions of traffic homogeneity and independence for ease of use. Based on our investigation of a real-world traffic dataset, we disprove these assumptions and point out the severe fidelity issue of the common practice that could invalidate many motivations and conclusions from influential research works. In this paper, we present Encore, a DCN traic modeling framework for ine-grained traic modeling and high-fidelity synthetic traffic generation. Leveraging machine learning techniques, Encore effectively extracts and preserves essential distribution and sequential features from raw traic. Preliminary experiments demonstrate that the traic generated by Encore not only restores the key features of real traffic but also achieves high consistency when used to evaluate network performance. We envision further expanding Encore to full-process traffic modeling and generation, and expect these critical improvements in traffic models can facilitate the DCN performance evaluation and optimization.
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Published In
November 2023
306 pages
ISBN:9798400704154
DOI:10.1145/3626111
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This work is licensed under a Creative Commons Attribution-NonCommercial International 4.0 License.
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Published: 28 November 2023
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HotNets '23: The 22nd ACM Workshop on Hot Topics in Networks
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MA, Cambridge, USA
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