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Published in:
2015 | OriginalPaper | Chapter
A Scalable Framework for Universal Data Generation in Parallel
Authors : Ling Gu, Minqi Zhou, Qiangqiang Kang, Aoying Zhou
Published in: Performance Characterization and Benchmarking. Traditional to Big Data
Publisher: Springer International Publishing
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Abstract
Nowadays, more and more companies, such as Amazon, Twitter and etc., are facing the big data problem, which requires higher performance to manage tremendous large data sets. Data management systems with a new architecture taking full advantages of computer hardware are emerging, on the purpose of maximizing the system performance and fulfilling customs’ current or even future requirements. How to test performance and confirm the suitability of the new data management system becomes a primary task of these companies. Hence, how to generate a scaled data set with desired volumes and in desired velocity effectively becomes a problem imperative to be solved, together with the goal to keep the characters of their real data set as many as possible (realistic). In this paper, we proposed PSUG to generate a realistic database in terms of required volume and velocity in a scalable parallel manner. Our extensive experimental studies confirm the efficiency and effectiveness of our proposed method.