CLC number: TP311.1
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2016-09-18
Cited: 0
Clicked: 8835
Long-xiang Wang, Xiao-she Dong, Xing-jun Zhang, Yin-feng Wang, Tao Ju, Guo-fu Feng. TextGen: a realistic text data content generation method for modern storage system benchmarks[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(10): 982-993.
@article{title="TextGen: a realistic text data content generation method for modern storage system benchmarks",
author="Long-xiang Wang, Xiao-she Dong, Xing-jun Zhang, Yin-feng Wang, Tao Ju, Guo-fu Feng",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="17",
number="10",
pages="982-993",
year="2016",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1500332"
}
%0 Journal Article
%T TextGen: a realistic text data content generation method for modern storage system benchmarks
%A Long-xiang Wang
%A Xiao-she Dong
%A Xing-jun Zhang
%A Yin-feng Wang
%A Tao Ju
%A Guo-fu Feng
%J Frontiers of Information Technology & Electronic Engineering
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%P 982-993
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%D 2016
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1500332
TY - JOUR
T1 - TextGen: a realistic text data content generation method for modern storage system benchmarks
A1 - Long-xiang Wang
A1 - Xiao-she Dong
A1 - Xing-jun Zhang
A1 - Yin-feng Wang
A1 - Tao Ju
A1 - Guo-fu Feng
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 17
IS - 10
SP - 982
EP - 993
%@ 2095-9184
Y1 - 2016
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1500332
Abstract: Modern storage systems incorporate data compressors to improve their performance and capacity. As a result, data content can significantly influence the result of a storage system benchmark. Because real-world proprietary datasets are too large to be copied onto a test storage system, and most data cannot be shared due to privacy issues, a benchmark needs to generate data synthetically. To ensure that the result is accurate, it is necessary to generate data content based on the characterization of real-world data properties that influence the storage system performance during the execution of a benchmark. The existing approach, called SDGen, cannot guarantee that the benchmark result is accurate in storage systems that have built-in word-based compressors. The reason is that SDGen characterizes the properties that influence compression performance only at the byte level, and no properties are characterized at the word level. To address this problem, we present TextGen, a realistic text data content generation method for modern storage system benchmarks. TextGen builds the word corpus by segmenting real-world text datasets, and creates a word-frequency distribution by counting each word in the corpus. To improve data generation performance, the word-frequency distribution is fitted to a lognormal distribution by maximum likelihood estimation. The Monte Carlo approach is used to generate synthetic data. The running time of TextGen generation depends only on the expected data size, which means that the time complexity of TextGen is O(n). To evaluate TextGen, four real-world datasets were used to perform an experiment. The experimental results show that, compared with SDGen, the compression performance and compression ratio of the datasets generated by TextGen deviate less from real-world datasets when end-tagged dense code, a representative of word-based compressors, is evaluated.
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